[ { "key": "R4Q5V8WP", "version": 28, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/R4Q5V8WP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/R4Q5V8WP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Griffiths and Boehm", "parsedDate": "2018-05-30", "numChildren": 0 }, "data": { "key": "R4Q5V8WP", "version": 28, "itemType": "conferencePaper", "title": "RAPID OBJECT DETECTION SYSTEMS, UTILISING DEEP LEARNING AND UNMANNED AERIAL SYSTEMS (UAS) FOR CIVIL ENGINEERING APPLICATIONS", "creators": [ { "creatorType": "author", "firstName": "D.", "lastName": "Griffiths" }, { "creatorType": "author", "firstName": "J.", "lastName": "Boehm" } ], "abstractNote": "
Abstract. With deep learning approaches now out-performing traditional image processing techniques for image understanding, this paper accesses the potential of rapid generation of Convolutional Neural Networks (CNNs) for applied engineering purposes. Three CNNs are trained on 275 UAS-derived and freely available online images for object detection of 3m2 segments of railway track. These includes two models based on the Faster RCNN object detection algorithm (Resnet and Incpetion-Resnet) as well as the novel onestage Focal Loss network architecture (Retinanet). Model performance was assessed with respect to three accuracy metrics. The first two consisted of Intersection over Union (IoU) with thresholds 0.5 and 0.1. The last assesses accuracy based on the proportion of track covered by object detection proposals against total track length. In under six hours of training (and two hours of manual labelling) the models detected 91.3 %, 83.1 % and 75.6 % of track in the 500 test images acquired from the UAS survey Retinanet, Resnet and Inception-Resnet respectively. We then discuss the potential for such applications of such systems within the engineering field for a range of scenarios.
", "date": "2018/05/30", "proceedingsTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "conferenceName": "ISPRS TC II Mid-term SymposiumTowards Photogrammetry 2020(Volume XLII-2) - 4–7 June 2018, Riva del Garda, Italy", "place": "", "publisher": "Copernicus GmbH", "volume": "XLII-2", "pages": "391-398", "series": "", "language": "English", "DOI": "https://doi.org/10.5194/isprs-archives-XLII-2-391-2018", "ISBN": "", "shortTitle": "", "url": "https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/391/2018/", "accessDate": "2020-02-04T17:44:08Z", "archive": "", "archiveLocation": "", "libraryCatalog": "www.int-arch-photogramm-remote-sens-spatial-inf-sci.net", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "2JSQGMDR" ], "relations": {}, "dateAdded": "2022-02-01T23:23:16Z", "dateModified": "2022-02-01T23:23:16Z" } }, { "key": "J3APVUAS", "version": 28, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/J3APVUAS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/J3APVUAS", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Griffiths and Boehm", "parsedDate": "2019-08-01", "numChildren": 0 }, "data": { "key": "J3APVUAS", "version": 28, "itemType": "journalArticle", "title": "Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours", "creators": [ { "creatorType": "author", "firstName": "David", "lastName": "Griffiths" }, { "creatorType": "author", "firstName": "Jan", "lastName": "Boehm" } ], "abstractNote": "Robust and reliable automatic building detection and segmentation from aerial images/point clouds has been a prominent field of research in remote sensing, computer vision and point cloud processing for a number of decades. One of the largest issues associated with deep learning methods is the high quantity of data required for training. To help address this we present a method to improve public GIS building footprint labels by using Morphological Geodesic Active Contours (MorphGACs). We demonstrate by improving the quality of building footprint labels for detection and semantic segmentation, more robust and reliable models can be obtained. We evaluate these methods over a large UK-based dataset of 24556 images containing 169835 building instances. This is achieved by training several Mask/Faster R-CNN and RetinaNet deep convolutional neural networks. Networks are supplied with both RGB and fused RGB-lidar data. We offer quantitative analysis on the benefits of the inclusion of depth data for building segmentation. By employing both methods we achieve a detection accuracy of 0.92 (mAP@0.5) and segmentation f1 scores of 0.94 over a 4911 test images ranging from urban to rural scenes.", "publicationTitle": "ISPRS Journal of Photogrammetry and Remote Sensing", "volume": "154", "issue": "", "pages": "70-83", "date": "August 1, 2019", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "ISPRS Journal of Photogrammetry and Remote Sensing", "language": "en", "DOI": "10.1016/j.isprsjprs.2019.05.013", "ISSN": "0924-2716", "shortTitle": "", "url": "http://www.sciencedirect.com/science/article/pii/S0924271619301352", "accessDate": "2020-01-14T14:24:55Z", "archive": "", "archiveLocation": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Aerial", "type": 1 }, { "tag": "Convolutional neural networks", "type": 1 }, { "tag": "Deep learning", "type": 1 }, { "tag": "Image processing", "type": 1 }, { "tag": "Lidar", "type": 1 }, { "tag": "Segmentation", "type": 1 } ], "collections": [ "KZJDAFI5" ], "relations": {}, "dateAdded": "2022-02-01T23:23:09Z", "dateModified": "2022-02-01T23:23:09Z" } }, { "key": "68ZZ8DKK", "version": 26, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/68ZZ8DKK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/68ZZ8DKK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Panella et al.", "parsedDate": "2022-03-01", "numChildren": 0 }, "data": { "key": "68ZZ8DKK", "version": 26, "itemType": "journalArticle", "title": "Semantic segmentation of cracks: Data challenges and architecture", "creators": [ { "creatorType": "author", "firstName": "Fabio", "lastName": "Panella" }, { "creatorType": "author", "firstName": "Aldo", "lastName": "Lipani" }, { "creatorType": "author", "firstName": "Jan", "lastName": "Boehm" } ], "abstractNote": "Deep Learning (DL) semantic image segmentation is a technique used in several fields of research. The present paper analyses semantic crack segmentation as a case study to review the up to date research on semantic segmentation in the presence of fine structures and the effectiveness of established approaches to address the inherent class imbalance issue. The established UNet architecture is tested against networks consisting exclusively of stacked convolution without pooling layers (straight networks), with regard to the resolution of their segmentation results. Dice and Focal losses are also compared against each other to evaluate their effectiveness on highly imbalanced data. With the same aim, dropout and data augmentation approaches are tested, as additional regularizing mechanisms, to address the uneven distribution of the dataset. The experiments show that the good selection of the loss function has more impact in handling the class imbalance and boosting the detection performance than all the other regularizers with regards to segmentation resolution. Moreover, UNet, the architecture considered as reference, clearly outperforms the networks with no pooling layers both in performance and training time. The authors argue that UNet architectures, compared to the networks with no pooling layers, achieve high detection performance at a very low cost in terms of training time. Therefore, the authors consider such architecture as the state of the art for semantic segmentation of cracks. On the other hand, once computational cost is not an issue anymore thanks to constant improvements of technology, the application of networks without pooling layers might become attractive again because of their simplicity of and high performance.", "publicationTitle": "Automation in Construction", "volume": "135", "issue": "", "pages": "104110", "date": "March 1, 2022", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Automation in Construction", "language": "en", "DOI": "10.1016/j.autcon.2021.104110", "ISSN": "0926-5805", "shortTitle": "Semantic segmentation of cracks", "url": "https://www.sciencedirect.com/science/article/pii/S0926580521005616", "accessDate": "2022-02-01T23:09:13Z", "archive": "", "archiveLocation": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Crack detection", "type": 1 }, { "tag": "Deep learning", "type": 1 }, { "tag": "Imbalanced data", "type": 1 }, { "tag": "Infrastructure monitoring", "type": 1 }, { "tag": "Semantic segmentation", "type": 1 } ], "collections": [ "2JSQGMDR" ], "relations": {}, "dateAdded": "2022-02-01T23:22:15Z", "dateModified": "2022-02-01T23:22:15Z" } }, { "key": "4BAYX9ER", "version": 24, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/4BAYX9ER", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/4BAYX9ER", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/274247/items/RQ378PUF", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "4BAYX9ER", "version": 24, "parentItem": "RQ378PUF", "itemType": "note", "note": "Comment: 6 pages, 4 figures, dataset white paper", "tags": [], "relations": {}, "dateAdded": "2020-02-04T17:46:58Z", "dateModified": "2020-02-04T17:46:58Z" } }, { "key": "RQ378PUF", "version": 24, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/RQ378PUF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/RQ378PUF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Griffiths and Boehm", "parsedDate": "2019-07-10", "numChildren": 1 }, "data": { "key": "RQ378PUF", "version": 24, "itemType": "journalArticle", "title": "SynthCity: A large scale synthetic point cloud", "creators": [ { "creatorType": "author", "firstName": "David", "lastName": "Griffiths" }, { "creatorType": "author", "firstName": "Jan", "lastName": "Boehm" } ], "abstractNote": "With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist which, if proved effective can be exploited. We therefore argue that research in this domain would be of significant use. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is assigned a label from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.", "publicationTitle": "arXiv:1907.04758 [cs]", "volume": "", "issue": "", "pages": "", "date": "2019-07-10", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "", "ISSN": "", "shortTitle": "SynthCity", "url": "http://arxiv.org/abs/1907.04758", "accessDate": "2020-02-04T17:40:10Z", "archive": "", "archiveLocation": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "arXiv: 1907.04758", "tags": [ { "tag": "Computer Science - Computer Vision and Pattern Recognition", "type": 1 } ], "collections": [ "KZJDAFI5" ], "relations": {}, "dateAdded": "2020-02-04T17:46:58Z", "dateModified": "2020-02-04T17:46:58Z" } }, { "key": "U2RYWRXH", "version": 24, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/U2RYWRXH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/U2RYWRXH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Griffiths and Boehm", "parsedDate": "2019-08-01", "numChildren": 0 }, "data": { "key": "U2RYWRXH", "version": 24, "itemType": "journalArticle", "title": "Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours", "creators": [ { "creatorType": "author", "firstName": "David", "lastName": "Griffiths" }, { "creatorType": "author", "firstName": "Jan", "lastName": "Boehm" } ], "abstractNote": "Robust and reliable automatic building detection and segmentation from aerial images/point clouds has been a prominent field of research in remote sensing, computer vision and point cloud processing for a number of decades. One of the largest issues associated with deep learning methods is the high quantity of data required for training. To help address this we present a method to improve public GIS building footprint labels by using Morphological Geodesic Active Contours (MorphGACs). We demonstrate by improving the quality of building footprint labels for detection and semantic segmentation, more robust and reliable models can be obtained. We evaluate these methods over a large UK-based dataset of 24556 images containing 169835 building instances. This is achieved by training several Mask/Faster R-CNN and RetinaNet deep convolutional neural networks. Networks are supplied with both RGB and fused RGB-lidar data. We offer quantitative analysis on the benefits of the inclusion of depth data for building segmentation. By employing both methods we achieve a detection accuracy of 0.92 (mAP@0.5) and segmentation f1 scores of 0.94 over a 4911 test images ranging from urban to rural scenes.", "publicationTitle": "ISPRS Journal of Photogrammetry and Remote Sensing", "volume": "154", "issue": "", "pages": "70-83", "date": "August 1, 2019", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "ISPRS Journal of Photogrammetry and Remote Sensing", "language": "en", "DOI": "10.1016/j.isprsjprs.2019.05.013", "ISSN": "0924-2716", "shortTitle": "", "url": "http://www.sciencedirect.com/science/article/pii/S0924271619301352", "accessDate": "2020-02-04T17:39:41Z", "archive": "", "archiveLocation": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Aerial", "type": 1 }, { "tag": "Convolutional neural networks", "type": 1 }, { "tag": "Deep learning", "type": 1 }, { "tag": "Image processing", "type": 1 }, { "tag": "Lidar", "type": 1 }, { "tag": "Segmentation", "type": 1 } ], "collections": [ "KZJDAFI5" ], "relations": {}, "dateAdded": "2020-02-04T17:46:58Z", "dateModified": "2020-02-04T17:46:58Z" } }, { "key": "9ITMX9HG", "version": 24, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/9ITMX9HG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/9ITMX9HG", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Griffiths and Boehm", "parsedDate": "2019-01", "numChildren": 0 }, "data": { "key": "9ITMX9HG", "version": 24, "itemType": "journalArticle", "title": "A Review on Deep Learning Techniques for 3D Sensed Data Classification", "creators": [ { "creatorType": "author", "firstName": "David", "lastName": "Griffiths" }, { "creatorType": "author", "firstName": "Jan", "lastName": "Boehm" } ], "abstractNote": "Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches, including RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.", "publicationTitle": "Remote Sensing", "volume": "11", "issue": "12", "pages": "1499", "date": "2019/1", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "en", "DOI": "10.3390/rs11121499", "ISSN": "", "shortTitle": "", "url": "https://www.mdpi.com/2072-4292/11/12/1499", "accessDate": "2020-02-04T17:42:06Z", "archive": "", "archiveLocation": "", "libraryCatalog": "www.mdpi.com", "callNumber": "", "rights": "http://creativecommons.org/licenses/by/3.0/", "extra": "", "tags": [ { "tag": "classification", "type": 1 }, { "tag": "deep learning", "type": 1 }, { "tag": "machine learning", "type": 1 }, { "tag": "point cloud", "type": 1 }, { "tag": "segmentation", "type": 1 }, { "tag": "semantics", "type": 1 } ], "collections": [ "KZJDAFI5" ], "relations": {}, "dateAdded": "2020-02-04T17:46:58Z", "dateModified": "2020-02-04T17:46:58Z" } }, { "key": "8VUJQFRL", "version": 24, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/8VUJQFRL", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/8VUJQFRL", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Griffiths and Boehm", "parsedDate": "2019-06-05", "numChildren": 0 }, "data": { "key": "8VUJQFRL", "version": 24, "itemType": "journalArticle", "title": "Weighted Point Cloud Augmentation for Neural Network Training Data Class-imbalance", "creators": [ { "creatorType": "author", "firstName": "D.", "lastName": "Griffiths" }, { "creatorType": "author", "firstName": "J.", "lastName": "Boehm" } ], "abstractNote": "Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds. However, many real-world point clouds contain a large class im-balance due to the natural class im-balance observed in nature. For example, a 3D scan of an urban environment will consist mostly of road and façade, whereas other objects such as poles will be under-represented. In this paper we address this issue by employing a weighted augmentation to increase classes that contain fewer points. By mitigating the class im-balance present in the data we demonstrate that a standard PointNet++ deep neural network can achieve higher performance at inference on validation data. This was observed as an increase of F1 score of 19% and 25% on two test benchmark datasets; ScanNet and Semantic3D respectively where no class im-balance pre-processing had been performed. Our networks performed better on both highly-represented and under-represented classes, which indicates that the network is learning more robust and meaningful features when the loss function is not overly exposed to only a few classes.", "publicationTitle": "The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "volume": "XLII-2", "issue": "W13", "pages": "981-987", "date": "2019-06-05", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "eng", "DOI": "", "ISSN": "1682-1750", "shortTitle": "", "url": "https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/981/2019/", "accessDate": "2020-02-04T17:41:19Z", "archive": "", "archiveLocation": "", "libraryCatalog": "discovery.ucl.ac.uk", "callNumber": "", "rights": "open", "extra": "", "tags": [], "collections": [ "KZJDAFI5" ], "relations": {}, "dateAdded": "2020-02-04T17:46:58Z", "dateModified": "2020-02-04T17:46:58Z" } }, { "key": "WUKAVT64", "version": 23, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/WUKAVT64", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/WUKAVT64", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Backes et al.", "parsedDate": "2019-06-04", "numChildren": 0 }, "data": { "key": "WUKAVT64", "version": 23, "itemType": "conferencePaper", "title": "TOWARDS A HIGH-RESOLUTION DRONE-BASED 3D MAPPING DATASET TO OPTIMISE FLOOD HAZARD MODELLING", "creators": [ { "creatorType": "author", "firstName": "D.", "lastName": "Backes" }, { "creatorType": "author", "firstName": "G.", "lastName": "Schumann" }, { "creatorType": "author", "firstName": "F. N.", "lastName": "Teferele" }, { "creatorType": "author", "firstName": "J.", "lastName": "Boehm" } ], "abstractNote": "
Abstract. The occurrence of urban flooding following strong rainfall events may increase as a result of climate change. Urban expansion, aging infrastructure and an increasing number of impervious surfaces are further exacerbating flooding. To increase resilience and support flood mitigation, bespoke accurate flood modelling and reliable prediction is required. However, flooding in urban areas is most challenging. State-of-the-art flood inundation modelling is still often based on relatively low-resolution 2.5 D bare earth models with 2–5 m GSD. Current systems suffer from a lack of precise input data and numerical instabilities and lack of other important data, such as drainage networks. Especially, the quality and resolution of the topographic input data represents a major source of uncertainty in urban flood modelling. A benchmark study is needed that defines the accuracy requirements for highly detailed urban flood modelling and to improve our understanding of important threshold processes and limitations of current methods and 3D mapping data alike.
This paper presents the first steps in establishing a new, innovative multiscale data set suitable to benchmark urban flood modelling. The final data set will consist of high-resolution 3D mapping data acquired from different airborne platforms, focusing on the use of drones (optical and LiDAR). The case study includes residential as well as rural areas in Dudelange/Luxembourg, which have been prone to localized flash flooding following strong rainfall events in recent years. The project also represents a cross disciplinary collaboration between the geospatial and flood modelling community. In this paper, we introduce the first steps to build up a new benchmark data set together with some initial flood modelling results. More detailed investigations will follow in the next phases of this project.
", "date": "2019/06/04", "proceedingsTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "conferenceName": "ISPRS Geospatial Week 2019 (Volume XLII-2/W13) - 10–14 June 2019, Enschede, The Netherlands", "place": "", "publisher": "Copernicus GmbH", "volume": "XLII-2-W13", "pages": "181-187", "series": "", "language": "English", "DOI": "https://doi.org/10.5194/isprs-archives-XLII-2-W13-181-2019", "ISBN": "", "shortTitle": "", "url": "https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/181/2019/", "accessDate": "2020-02-04T17:40:41Z", "archive": "", "archiveLocation": "", "libraryCatalog": "www.int-arch-photogramm-remote-sens-spatial-inf-sci.net", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "2JSQGMDR" ], "relations": {}, "dateAdded": "2020-02-04T17:46:47Z", "dateModified": "2020-02-04T17:46:47Z" } }, { "key": "F5GL25US", "version": 23, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/F5GL25US", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/F5GL25US", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Ortiz Arteaga et al.", "parsedDate": "2019-11-29", "numChildren": 0 }, "data": { "key": "F5GL25US", "version": 23, "itemType": "conferencePaper", "title": "INITIAL INVESTIGATION OF A LOW-COST AUTOMOTIVE LIDAR SYSTEM", "creators": [ { "creatorType": "author", "firstName": "A.", "lastName": "Ortiz Arteaga" }, { "creatorType": "author", "firstName": "D.", "lastName": "Scott" }, { "creatorType": "author", "firstName": "J.", "lastName": "Boehm" } ], "abstractNote": "Abstract. This investigation focuses on the performance assessment of a low-cost automotive LIDAR, the Livox Mid-40 series. The work aims to examine the qualities of the sensor in terms of ranging, repeatability and accuracy. Towards these aims a series of experiments were carried out based on previous research of low-cost sensor accuracy, LIDAR accuracy investigation and TLS calibration experiments. The Livox Mid-40 series offers the advantage of a long-range detection beyond 200 m at a remarkably low cost. The preliminary results of the tests for this sensor indicate that it can be used for reality capture purposes such as to obtain coarse as-built plans and volume calculations to mention a few. Close-range experiments were conducted in an indoor laboratory setting. Long-range experiments were performed outdoors towards a building façade. Reference values in both setups were provided with a Leica RTC 360 terrestrial LIDAR system. In the close-range experiments a cross section of the point cloud shows a significant level of noise in the acquired data. At a stand-off distance of 5 m the length measurement tests reveal deviations of up to 11 mm to the reference values. Range measurement was tested up to 130 meters and shows ranging deviations of up to 25 millimetres. The authors recommend further investigation of the issues in radiometric behaviour and material reflectivity. Also, more knowledge about the internal components is needed to understand the causes of the concentric ripple effect observed at close ranges. Another aspect that should be considered is the use of targets and their design as the non-standard scan pattern prevents automated detection with standard commercial software.
", "date": "2019/11/29", "proceedingsTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "conferenceName": "ISPRS TC IIAbstract. This paper analyses the use of BIM in heritage buildings, assessing the state-of-the-art and finding paths for further development. Specifically, this work is part of a broader project, which final aim is to support stakeholders through BIM. Given that humidity is one of the major causes of weathering, being able to detect, depict and forecast it, is a key task. A BIM model of a heritage building – enhanced with the integration of a weathering forecasting model – will be able to give detailed information on possible degradation patterns, and when they will happen. This information can be effectively used to plan both ordinary and extraordinary maintenance. The Jewel Tower in London, our case study, is digitised using combined laser scanning and photogrammetry, and a virtual model is produced. The point cloud derived from combined laser scanning & photogrammetry is traced out in with Autodesk Revit, where the main volumetry (gross walls and floors) is created with parametric objects. Surface characterisation of the façade is given through renderings. Specifically, new rendering materials have been created for this purpose, based on rectified photos of the Tower. The model is then integrated with moisture data, organised in spreadsheets and linked to it via parametric objects representing the points where measurements had been previously taken. The spatial distribution of moisture is then depicted using Dynamo. This simple exercise demonstrates the potential Dynamo has for condition reporting, and future work will concentrate on the creation of a complex forecasting model to be linked through it.
", "date": "2018/05/30", "proceedingsTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "conferenceName": "ISPRS TC II Mid-term SymposiumTowards Photogrammetry 2020(Volume XLII-2) - 4–7 June 2018, Riva del Garda, Italy", "place": "", "publisher": "Copernicus GmbH", "volume": "XLII-2", "pages": "909-916", "series": "", "language": "English", "DOI": "https://doi.org/10.5194/isprs-archives-XLII-2-909-2018", "ISBN": "", "shortTitle": "", "url": "https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/909/2018/", "accessDate": "2020-02-04T17:43:35Z", "archive": "", "archiveLocation": "", "libraryCatalog": "www.int-arch-photogramm-remote-sens-spatial-inf-sci.net", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "W4ZZ6TRD" ], "relations": {}, "dateAdded": "2020-02-04T17:46:09Z", "dateModified": "2020-02-04T17:46:09Z" } }, { "key": "H8PVD9U7", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/H8PVD9U7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/H8PVD9U7", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Thomson and Boehm", "parsedDate": "2014", "numChildren": 0 }, "data": { "key": "H8PVD9U7", "version": 21, "itemType": "conferencePaper", "title": "Indoor Modelling Benchmark for 3D Geometry Extraction", "creators": [ { "creatorType": "author", "firstName": "C", "lastName": "Thomson" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" }, { "creatorType": "editor", "firstName": "F", "lastName": "Remondino" }, { "creatorType": "editor", "firstName": "F", "lastName": "Menna" } ], "abstractNote": "A combination of faster, cheaper and more accurate hardware, more sophisticated software, and greater industry acceptance have all laid the foundations for an increased desire for accurate 3D parametric models of buildings. Pointclouds are the data source of choice currently with static terrestrial laser scanning the predominant tool for large, dense volume measurement. The current importance of pointclouds as the primary source of real world representation is endorsed by CAD software vendor acquisitions of pointcloud engines in 2011. Both the capture and modelling of indoor environments require great effort in time by the operator (and therefore cost). Automation is seen as a way to aid this by reducing the workload of the user and some commercial packages have appeared that provide automation to some degree. In the data capture phase, advances in indoor mobile mapping systems are speeding up the process, albeit currently with a reduction in accuracy. As a result this paper presents freely accessible pointcloud datasets of two typical areas of a building each captured with two different capture methods and each with an accurate wholly manually created model. These datasets are provided as a benchmark for the research community to gauge the performance and improvements of various techniques for indoor geometry extraction. With this in mind, non-proprietary, interoperable formats are provided such as E57 for the scans and IFC for the reference model. The datasets can be found at: http://indoor-bench.github.io/indoor-bench", "date": "2014", "proceedingsTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "conferenceName": "", "place": "Dept. of Civil, Environmental & Geomatic Engineering (CEGE), University College London, Gower Street, London, WC1E 6BT, UK", "publisher": "Riva del Garda, Italy", "volume": "XL-5", "pages": "581–587", "series": "", "language": "", "DOI": "10.5194/isprsarchives-XL-5-581-2014", "ISBN": "", "shortTitle": "", "url": "http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/581/2014/", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "Reconstruction" }, { "tag": "bim" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "W4ZZ6TRD" ], "relations": {}, "dateAdded": "2017-04-10T14:00:12Z", "dateModified": "2017-04-10T14:00:12Z" } }, { "key": "U3ANW5MG", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/U3ANW5MG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/U3ANW5MG", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Thomson and Boehm", "parsedDate": "2015-09", "numChildren": 0 }, "data": { "key": "U3ANW5MG", "version": 21, "itemType": "journalArticle", "title": "Automatic Geometry Generation from Point Clouds for BIM", "creators": [ { "creatorType": "author", "firstName": "CPH", "lastName": "Thomson" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" }, { "creatorType": "editor", "firstName": "F", "lastName": "Remondino" }, { "creatorType": "editor", "firstName": "D", "lastName": "Gonzalez-Aguilera" }, { "creatorType": "editor", "firstName": "H", "lastName": "Lorenzo" }, { "creatorType": "editor", "firstName": "N", "lastName": "Kerle" }, { "creatorType": "editor", "firstName": "PS", "lastName": "Thenkabail" } ], "abstractNote": "The need for better 3D documentation of the built environment has come to the fore in recent years, led primarily by city modelling at the large scale and Building Information Modelling (BIM) at the smaller scale. Automation is seen as desirable as it removes the time-consuming and therefore costly amount of human intervention in the process of model generation. BIM is the focus of this paper as not only is there a commercial need, as will be shown by the number of commercial solutions, but also wide research interest due to the aspiration of automated 3D models from both Geomatics and Computer Science communities. The aim is to go beyond the current labour-intensive tracing of the point cloud to an automated process that produces geometry that is both open and more verifiable. This work investigates what can be achieved today with automation through both literature review and by proposing a novel point cloud processing process. We present an automated workflow for the generation of BIM data from 3D point clouds. We also present quality indicators for reconstructed geometry elements and a framework in which to assess the quality of the reconstructed geometry against a reference.", "publicationTitle": "Remote Sensing", "volume": "7", "issue": "9", "pages": "11753–11775", "date": "September 2015", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "10.3390/rs70911753", "ISSN": "2072-4292", "shortTitle": "", "url": "http://www.mdpi.com/2072-4292/7/9/11753/", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "bim" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "W4ZZ6TRD" ], "relations": {}, "dateAdded": "2017-04-10T14:00:12Z", "dateModified": "2017-04-10T14:00:12Z" } }, { "key": "PUNWV9EA", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/PUNWV9EA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/PUNWV9EA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Luhmann et al.", "parsedDate": "2013-11", "numChildren": 0 }, "data": { "key": "PUNWV9EA", "version": 21, "itemType": "book", "title": "Close-Range Photogrammetry and 3D Imaging", "creators": [ { "creatorType": "author", "firstName": "T", "lastName": "Luhmann" }, { "creatorType": "author", "firstName": "S", "lastName": "Robson" }, { "creatorType": "author", "firstName": "S", "lastName": "Kyle" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" } ], "abstractNote": "This is the second edition of the established guide to close-range photogrammetry which uses accurate imaging techniques to analyse the three-dimensional shape of a wide range of manufactured and natural objects. After more than 20 years of use, close-range photogrammetry, now for the most part entirely digital, has become an accepted, powerful and readily available technique for engineers, scientists and others who wish to utilise images to make accurate 3D measurements of complex objects. Here they will find the photogrammetric fundamentals, details of system hardware and software, and broad range of real-world applications in order to achieve this. Following the introduction, the book provides fundamental mathematics covering subjects such as image orientation, digital imaging processing and 3D reconstruction methods, as well as a discussion of imaging technology, including targeting and illumination, and its implementation in hardware and software. It concludes with an overview of photogrammetric solutions for typical applications in engineering, manufacturing, medical science, architecture, archaeology and other fields.", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "2nd", "place": "", "publisher": "De Gruyter", "date": "November 2013", "numPages": "", "language": "", "ISBN": "978-3-11-030269-1", "shortTitle": "", "url": "http://www.degruyter.com/view/product/203264", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "photogrammetry" }, { "tag": "public" } ], "collections": [ "2JSQGMDR" ], "relations": {}, "dateAdded": "2017-04-10T13:59:59Z", "dateModified": "2017-04-10T13:59:59Z" } }, { "key": "RQRZGKUZ", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/RQRZGKUZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/RQRZGKUZ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Liu and Boehm", "parsedDate": "2015-08", "numChildren": 0 }, "data": { "key": "RQRZGKUZ", "version": 21, "itemType": "journalArticle", "title": "CLASSIFICATION OF BIG POINT CLOUD DATA USING CLOUD COMPUTING", "creators": [ { "creatorType": "author", "firstName": "K", "lastName": "Liu" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" } ], "abstractNote": "", "publicationTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "volume": "XL-3/W3", "issue": "", "pages": "553–557", "date": "August 2015", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "10.5194/isprsarchives-XL-3-W3-553-2015", "ISSN": "", "shortTitle": "", "url": "http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W3/553/2015/isprsarchives-XL-3-W3-553-2015.html", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "big data" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "GMA7H7H4" ], "relations": {}, "dateAdded": "2017-04-10T13:59:55Z", "dateModified": "2017-04-10T13:59:55Z" } }, { "key": "KHW96PDQ", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/KHW96PDQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/KHW96PDQ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Liu and Boehm", "parsedDate": "2014", "numChildren": 0 }, "data": { "key": "KHW96PDQ", "version": 21, "itemType": "conferencePaper", "title": "A New Framework For Interactive Segmentation of Point Clouds", "creators": [ { "creatorType": "author", "firstName": "K", "lastName": "Liu" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" } ], "abstractNote": "", "date": "2014", "proceedingsTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "conferenceName": "", "place": "", "publisher": "", "volume": "XL-5", "pages": "", "series": "", "language": "", "DOI": "10.5194/isprsarchives-XL-5-357-2014", "ISBN": "", "shortTitle": "", "url": "http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-5/357/2014/", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "big data" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "KZJDAFI5" ], "relations": {}, "dateAdded": "2017-04-10T13:59:52Z", "dateModified": "2017-04-10T13:59:52Z" } }, { "key": "GQ3BFVUD", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/GQ3BFVUD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/GQ3BFVUD", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Liu et al.", "parsedDate": "2016-01", "numChildren": 0 }, "data": { "key": "GQ3BFVUD", "version": 21, "itemType": "journalArticle", "title": "CHANGE DETECTION OF MOBILE LIDAR DATA USING CLOUD COMPUTING", "creators": [ { "creatorType": "author", "firstName": "K", "lastName": "Liu" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" }, { "creatorType": "author", "firstName": "C", "lastName": "Alis" }, { "creatorType": "editor", "firstName": "L", "lastName": "Halounova" }, { "creatorType": "editor", "firstName": "K", "lastName": "Schindler" }, { "creatorType": "editor", "firstName": "A", "lastName": "Limpouch" }, { "creatorType": "editor", "firstName": "T", "lastName": "Pajdla" }, { "creatorType": "editor", "firstName": "V", "lastName": "Safar" }, { "creatorType": "editor", "firstName": "H", "lastName": "Mayer" }, { "creatorType": "editor", "firstName": "SO", "lastName": "Elberink" }, { "creatorType": "editor", "firstName": "C", "lastName": "Mallet" }, { "creatorType": "editor", "firstName": "F", "lastName": "Rottensteiner" }, { "creatorType": "editor", "firstName": "M", "lastName": "Bredif" } ], "abstractNote": "", "publicationTitle": "XXIII ISPRS CONGRESS, COMMISSION III", "volume": "41", "issue": "B3", "pages": "309–313", "date": "January 2016", "series": "International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "English", "DOI": "10.5194/isprsarchives-XLI-B3-309-2016", "ISSN": "2194-9034", "shortTitle": "", "url": "http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000392743800048&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=f41074198c063036414efcbc916f8956", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "LIDAR" }, { "tag": "big data" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "GMA7H7H4" ], "relations": {}, "dateAdded": "2017-04-10T13:59:48Z", "dateModified": "2017-04-10T13:59:48Z" } }, { "key": "Q4F7SDVK", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/Q4F7SDVK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/Q4F7SDVK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Böhm et al.", "parsedDate": "2016-06", "numChildren": 0 }, "data": { "key": "Q4F7SDVK", "version": 21, "itemType": "journalArticle", "title": "The Iqmulus urban showcase: Automatic tree classification and identification in huge mobile mapping point clouds", "creators": [ { "creatorType": "author", "firstName": "J", "lastName": "Böhm" }, { "creatorType": "author", "firstName": "M", "lastName": "Bredif" }, { "creatorType": "author", "firstName": "T", "lastName": "Gierlinger" }, { "creatorType": "author", "firstName": "M", "lastName": "Krämer" }, { "creatorType": "author", "firstName": "R", "lastName": "Lindenbergh" }, { "creatorType": "author", "firstName": "K", "lastName": "Liu" }, { "creatorType": "author", "firstName": "F", "lastName": "Michel" }, { "creatorType": "author", "firstName": "B", "lastName": "Sirmacek" } ], "abstractNote": "Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling \" 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.", "publicationTitle": "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives", "volume": "41", "issue": "", "pages": "301–307", "date": "June 2016", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "10.5194/isprsarchives-XLI-B3-301-2016", "ISSN": "1682-1750", "shortTitle": "", "url": "", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "big data" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "GMA7H7H4" ], "relations": {}, "dateAdded": "2017-04-10T13:59:45Z", "dateModified": "2017-04-10T13:59:45Z" } }, { "key": "IFMH6ZMN", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/IFMH6ZMN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/IFMH6ZMN", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Boehm", "parsedDate": "2014-05", "numChildren": 0 }, "data": { "key": "IFMH6ZMN", "version": 21, "itemType": "journalArticle", "title": "Accuracy Investigation for Structured-light Based Consumer 3D Sensors", "creators": [ { "creatorType": "author", "firstName": "J", "lastName": "Boehm" } ], "abstractNote": "", "publicationTitle": "Photogrammetrie - Fernerkundung - Geoinformation", "volume": "2014", "issue": "2", "pages": "117–127", "date": "May 2014", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "10.1127/1432-8364/2014/0214", "ISSN": "", "shortTitle": "", "url": "", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "photogrammetry" }, { "tag": "public" } ], "collections": [ "KZJDAFI5" ], "relations": {}, "dateAdded": "2017-04-10T13:59:42Z", "dateModified": "2017-04-10T13:59:42Z" } }, { "key": "7CNMFTHK", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/7CNMFTHK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/7CNMFTHK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Boehm and Liu", "parsedDate": "2015-08", "numChildren": 0 }, "data": { "key": "7CNMFTHK", "version": 21, "itemType": "journalArticle", "title": "NOSQL FOR STORAGE AND RETRIEVAL OF LARGE LIDAR DATA COLLECTIONS", "creators": [ { "creatorType": "author", "firstName": "J", "lastName": "Boehm" }, { "creatorType": "author", "firstName": "K", "lastName": "Liu" } ], "abstractNote": "", "publicationTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "volume": "XL-3/W3", "issue": "", "pages": "577–582", "date": "August 2015", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "10.5194/isprsarchives-XL-3-W3-577-2015", "ISSN": "", "shortTitle": "", "url": "http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W3/577/2015/isprsarchives-XL-3-W3-577-2015.html", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "big data" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "GMA7H7H4" ], "relations": {}, "dateAdded": "2017-04-10T13:59:39Z", "dateModified": "2017-04-10T13:59:39Z" } }, { "key": "T44MWHHD", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/T44MWHHD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/T44MWHHD", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Boehm et al.", "parsedDate": "2016-06", "numChildren": 0 }, "data": { "key": "T44MWHHD", "version": 21, "itemType": "journalArticle", "title": "SIDELOADING – INGESTION OF LARGE POINT CLOUDS INTO THE APACHE SPARK BIG DATA ENGINE", "creators": [ { "creatorType": "author", "firstName": "J", "lastName": "Boehm" }, { "creatorType": "author", "firstName": "K", "lastName": "Liu" }, { "creatorType": "author", "firstName": "C", "lastName": "Alis" } ], "abstractNote": "", "publicationTitle": "ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "volume": "XLI-B2", "issue": "", "pages": "343–348", "date": "June 2016", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "10.5194/isprsarchives-XLI-B2-343-2016", "ISSN": "", "shortTitle": "", "url": "", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "3D imaging" }, { "tag": "big data" }, { "tag": "point cloud" }, { "tag": "public" } ], "collections": [ "GMA7H7H4" ], "relations": {}, "dateAdded": "2017-04-10T13:59:39Z", "dateModified": "2017-04-10T13:59:39Z" } }, { "key": "S2DN5MZV", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/S2DN5MZV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/S2DN5MZV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Baik and Boehm", "parsedDate": "2017-03", "numChildren": 0 }, "data": { "key": "S2DN5MZV", "version": 21, "itemType": "bookSection", "title": "Jeddah Heritage Building Information Modelling (JHBIM)", "creators": [ { "creatorType": "author", "firstName": "A", "lastName": "Baik" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" } ], "abstractNote": "This book is about Heritage Building Information Modelling (HBIM), which necessarily differs from the commonplace applications of BIM to new construction. Where BIM is being used, the focus is still very much on design and construction.", "bookTitle": "Heritage Building Information Modelling", "series": "", "seriesNumber": "11", "volume": "", "numberOfVolumes": "", "edition": "", "place": "", "publisher": "Routledge", "date": "March 2017", "pages": "", "language": "", "ISBN": "978-1-138-64568-4", "shortTitle": "", "url": "", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "HBIM" }, { "tag": "bim" }, { "tag": "public" } ], "collections": [ "W4ZZ6TRD" ], "relations": {}, "dateAdded": "2017-04-10T13:59:34Z", "dateModified": "2017-04-10T13:59:34Z" } }, { "key": "7RH9CQ5D", "version": 21, "library": { "type": "group", "id": 274247, "name": "3D Imaging (public)", "links": { "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/274247/items/7RH9CQ5D", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/3d_imaging_public/items/7RH9CQ5D", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1129344, "username": "janboehm", "name": "Jan Boehm", "links": { "alternate": { "href": "https://www.zotero.org/janboehm", "type": "text/html" } } }, "creatorSummary": "Baik and Boehm", "parsedDate": "2016-02", "numChildren": 0 }, "data": { "key": "7RH9CQ5D", "version": 21, "itemType": "journalArticle", "title": "Building information modelling for historical building Historic Jeddah - Saudi Arabia", "creators": [ { "creatorType": "author", "firstName": "A", "lastName": "Baik" }, { "creatorType": "author", "firstName": "J", "lastName": "Boehm" } ], "abstractNote": "", "publicationTitle": "2015 Digital Heritage", "volume": "", "issue": "", "pages": "", "date": "February 2016", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "language": "", "DOI": "10.1109/digitalheritage.2015.7419468", "ISSN": "", "shortTitle": "", "url": "http://dx.doi.org/10.1109/digitalheritage.2015.7419468", "accessDate": "", "archive": "", "archiveLocation": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "HBIM" }, { "tag": "bim" }, { "tag": "public" } ], "collections": [ "W4ZZ6TRD" ], "relations": {}, "dateAdded": "2017-04-10T13:59:34Z", "dateModified": "2017-04-10T13:59:34Z" } } ]