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Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images

Marjan Alirezaie, Martin Längkvist, Andrey Kiselev, Amy Loutfi
2016 International Conference Geographic Information Science  
In this paper, we summarize the practical issues met during our interaction with OpenStreetMap for the purpose of automatically generating labelled data used by data classification methods.  ...  For instance, given a satellite imagery scene containing several objects, a pixel-based classification method may need a ground truth in the scale of the scene's number of pixels, whereas a ground truth  ...  Providing a reliable and precise ground truth for large city-wide size satellite imagery data on a pixel-level is non-trivial and time consuming.  ... 
dblp:conf/giscience/AlirezaieLKL16 fatcat:toazndb2ufeztgje3blkcur6pu


M. Bosch, A. Leichtman, D. Chilcott, H. Goldberg, M. Brown
2017 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Sources of ground truth include airborne lidar and overhead imagery, and we demonstrate a semi-automated process for producing accurate ground truth shape files to characterize building footprints.  ...  We now define a more complete metric evaluation pipeline developed as publicly available open source software to assess semantically labeled 3D models of complex urban scenes derived from multi-view commercial  ...  ACKNOWLEDGEMENTS The commercial satellite imagery in the public benchmark data set was provided courtesy of DigitalGlobe. The ground truth lidar data was provided courtesy of IARPA.  ... 
doi:10.5194/isprs-archives-xlii-1-w1-239-2017 fatcat:7i5dewed6jht5ayk4ukzj4i7xa

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning [article]

Conrad M Albrecht, Fernando Marianno, Levente J Klein
2022 arXiv   pre-print
A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data.  ...  As proof of concept, we utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas with multiple land cover classes.  ...  Geospatial Platforms Open-source geospatial data volume exceeds petabytes making it comparable in volume to data generated by social media [33] .  ... 
arXiv:2202.00067v1 fatcat:ytf3422ggbfctbeqaxgz3gby3e

SpaceNet: A Remote Sensing Dataset and Challenge Series [article]

Adam Van Etten, Dave Lindenbaum, Todd M. Bacastow
2019 arXiv   pre-print
Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs.  ...  Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet.  ...  by open source routing tools.  ... 
arXiv:1807.01232v3 fatcat:jzt2emz3vvgx3khyjvyxmeuffa

Mapping Urban Land Use in India and Mexico using Remote Sensing and Machine Learning

Peter Kerins, Brook Guzder-Williams, Eric Mackres, Taufiq Rashid, Eric Pietraszkiewicz
2021 WRI Publications  
This technical note describes the data sources and methodology underpinning a computer system for the automated generation of land use/land cover (LULC) maps of urban areas based on medium-resolution (  ...  Distinguishing between recognizable, clearly defined types of land use within a built-up area, rather than merely delineating artificial land cover, enables a huge variety of potential applications for  ...  ACKNOWLEDGMENTS We would like to thank the National Geographic Society's Geographic Visualization Lab for both funding and counsel for this research.  ... 
doi:10.46830/writn.20.00048 fatcat:e7uksq2lg5dnjnuse23xvc66di

Generating a Training Dataset for Land Cover Classification to Advance Global Development [article]

Yoni Nachmany, Hamed Alemohammad
2018 arXiv   pre-print
Scene-level classifications were predicted by Random Forests trained on valid reflectance data and the filtered labels, and achieved over 80% model accuracy for a variety of locations.  ...  The goal is to create a sustained community-wide effort to generate image labels not only for land cover, but also very specific images for major agriculture crops across the world and other thematic categories  ...  Stanislaw Lewinski from Space Research Centre of Polish Academy of Sciences and PI of S2GLC for his recommendations.  ... 
arXiv:1811.07998v1 fatcat:6xpqvkvxlzfgzbmle3iwjru74a


R. Can, S. Kocaman, A. O. Ok
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The automation of geoinformation (GI) collection and interpretation has been a fundamental goal for many researchers.  ...  In this study, a web-based geospatial AI (GeoAI) platform was developed for map updating by using the image processing results obtained from a DL algorithm to assist volunteers.  ...  ACKNOWLEDGEMENTS This research is conducted as a part of the M.Sc. thesis of the first author.  ... 
doi:10.5194/isprs-archives-xliii-b5-2021-13-2021 fatcat:a6pvrl4b65cq5czhqgfvlhkttq

Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability [article]

Abraham Noah Wu, Filip Biljecki
2021 arXiv   pre-print
To tackle this issue, we introduce Roofpedia, a set of three contributions: (i) automatic mapping of relevant urban roof typology from satellite imagery; (ii) an open roof registry mapping the spatial  ...  Sustainable roofs, such as those with greenery and photovoltaic panels, contribute to the roadmap for reducing the carbon footprint of cities.  ...  This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start-Up Grant R-295-000-171-133.  ... 
arXiv:2012.14349v3 fatcat:pdtmpugdtjgkvcph5jpnvpu52i

Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability

Abraham Noah Wu, Filip Biljecki
2021 Landscape and Urban Planning  
contributing to the sustainable development of cities.  ...  Landscape and Urban Planning 214 (2021) 104167 2 climate pledges, estimate carbon offset capacities of cities, and ultimately support better policies and strategies to increase the adoption of instruments  ...  This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start-Up Grant R-295-000-171-133.  ... 
doi:10.1016/j.landurbplan.2021.104167 fatcat:nv76dy5jxjfq7gai5ruz6tq2em

Multi-label Pixelwise Classification for Reconstruction of Large-scale Urban Areas [article]

Yuanlie He, Sudhir Mudur, Charalambos Poullis
2018 arXiv   pre-print
A supervised learning approach is followed for training a 13-layer CNN using both LiDAR and satellite images.  ...  Object classification is one of the many holy grails in computer vision and as such has resulted in a very large number of algorithms being proposed already.  ...  Markus Gerke for creating the benchmark and making it publicly available. We also thank Microsoft Research for making processing large datasets possible through its Azure for Research Award.  ... 
arXiv:1709.07368v2 fatcat:db72ozyourhb5n76ktocsf7dgq


Bart Thomee, Benjamin Elizalde, David A. Shamma, Karl Ni, Gerald Friedland, Douglas Poland, Damian Borth, Li-Jia Li
2016 Communications of the ACM  
In this article we explain the rationale behind its creation, as well as the implications the dataset has for science, research, engineering, and development.  ...  The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license.  ...  ACKNOWLEDGMENTS We would like to thank Planet for providing us with high resolution satellite images for this task. Additionally, this work was supported BMBF project MOM (Grant 01IW15002).  ... 
doi:10.1145/2812802 fatcat:bilsg4ziejaa5p2t3dnhi34hqi

Model Generalization in Deep Learning Applications for Land Cover Mapping [article]

Lucas Hu, Caleb Robinson, Bistra Dilkina
2021 arXiv   pre-print
Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery.  ...  takeaways for future satellite imagery-related applications.  ...  ACKNOWLEDGMENTS The authors would like to thank Michael Schmitt and Chunping Qiu for their help in providing the starter code for both the FC-DenseNet land-use classification models [8] .  ... 
arXiv:2008.10351v3 fatcat:67momfmllzh2fbvoggsthrrnnq

Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale [article]

Adrian Albert and Jasleen Kaur and Marta Gonzalez
2017 arXiv   pre-print
For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the  ...  We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data  ...  Data sampling and acquisition We set out to develop a strategy to obtain high-quality samples of the type (satellite image, ground truth label) to use in training convolutional architectures for image  ... 
arXiv:1704.02965v2 fatcat:fnwamxkr6rbutgmsxyqgspuc4e

Real-Time Crisis Mapping of Natural Disasters Using Social Media

Stuart E. Middleton, Lee Middleton, Stefano Modafferi
2014 IEEE Intelligent Systems  
We take locations from gazetteer, street map and volunteered geographic information (VGI) sources for areas at risk of disaster and match them to geo-parsed real-time tweet data streams.  ...  We present a social media crisis mapping platform for natural disasters.  ...  Stefano Modafferi is a senior research engineer at the University of Southampton IT Innovation Centre. His main research interests are information modelling and software architectures.  ... 
doi:10.1109/mis.2013.126 fatcat:6w4esamhbvewnmftxzekbvbf2i

A Unified Model for Near and Remote Sensing

Scott Workman, Menghua Zhai, David J. Crandall, Nathan Jacobs
2017 2017 IEEE International Conference on Computer Vision (ICCV)  
The output of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image.  ...  We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use.  ...  to the University of Kentucky Center for Computational Sciences.  ... 
doi:10.1109/iccv.2017.293 dblp:conf/iccv/WorkmanZCJ17 fatcat:3c2fcr5hnjazbepiqlwa5aqvte
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