publication . Preprint . 2018

SpaceNet: A Remote Sensing Dataset and Challenge Series

Van Etten, Adam; Lindenbaum, Dave; Bacastow, Todd M.;
Open Access English
  • Published: 03 Jul 2018
Abstract
Comment: 10 pages, 5 figures, 2 tables, 5 appendices
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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25 references, page 1 of 2

[1] AWS: Spacenet on aws. https://registry.opendata.aws/spacenet/ (2017)

[2] Zhang, F., Du, B., Zhang, L.: A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data. CoRR abs/1702.07985 (2017)

[3] Tatem, A.J.: Worldpop, open data for spatial demography. Scientific Data 4 (01 2017) 170004 EP - [OpenAIRE]

[4] Stevens, F.R., Gaughan, A.E., Linard, C., Tatem, A.J.: Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLOS ONE 10(2) (02 2015) 1-22

[5] HOTOSM: Hot tasking manager. https://tasks.hotosm.org/ (2018)

[6] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115(3) (2015) 211-252

[7] ISPRS: 2d semantic labeling contest. http://www2.isprs.org/commissions/comm3/ wg4/semantic-labeling.html (2018)

[8] Wang, S., Bai, M., Máttyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., Urtasun, R.: Torontocity: Seeing the world with a million eyes. CoRR abs/1612.00423 (2016)

[9] Mnih, V.: Machine Learning for Aerial Image Labeling. PhD thesis, University of Toronto (2013)

[10] Mundhenk, T.N., Konjevod, G., Sakla, W.A., Boakye, K.: A large contextual dataset for classification, detection and counting of cars with deep learning. CoRR abs/1609.04453 (2016) [OpenAIRE]

[11] Lam, D., Kuzma, R., McGee, K., Dooley, S., Laielli, M., Klaric, M., Bulatov, Y., McCord, B.: xview: Objects in context in overhead imagery. CoRR abs/1802.07856 (2018) [OpenAIRE]

[12] CosmiQWorks: Spacenet challenge utilites. https://github.com/SpaceNetChallenge/ utilities/ (2018)

[13] Christie, G., Fendley, N., Wilson, J., Mukherjee, R.: Functional map of the world. In: CVPR. (2018)

[14] Goldberg, H., Brown, M., Wang, S.: A benchmark for building footprint classification using orthorectified rgb imagery and digital surface models from commercial satellites. In: Proceedings of IEEE Applied Imagery Pattern Recognition Workshop 2017. (2017)

[15] CosmiQWorks: Apls metric. https://github.com/CosmiQ/apls (2017)

25 references, page 1 of 2
Abstract
Comment: 10 pages, 5 figures, 2 tables, 5 appendices
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from
25 references, page 1 of 2

[1] AWS: Spacenet on aws. https://registry.opendata.aws/spacenet/ (2017)

[2] Zhang, F., Du, B., Zhang, L.: A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data. CoRR abs/1702.07985 (2017)

[3] Tatem, A.J.: Worldpop, open data for spatial demography. Scientific Data 4 (01 2017) 170004 EP - [OpenAIRE]

[4] Stevens, F.R., Gaughan, A.E., Linard, C., Tatem, A.J.: Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLOS ONE 10(2) (02 2015) 1-22

[5] HOTOSM: Hot tasking manager. https://tasks.hotosm.org/ (2018)

[6] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115(3) (2015) 211-252

[7] ISPRS: 2d semantic labeling contest. http://www2.isprs.org/commissions/comm3/ wg4/semantic-labeling.html (2018)

[8] Wang, S., Bai, M., Máttyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., Urtasun, R.: Torontocity: Seeing the world with a million eyes. CoRR abs/1612.00423 (2016)

[9] Mnih, V.: Machine Learning for Aerial Image Labeling. PhD thesis, University of Toronto (2013)

[10] Mundhenk, T.N., Konjevod, G., Sakla, W.A., Boakye, K.: A large contextual dataset for classification, detection and counting of cars with deep learning. CoRR abs/1609.04453 (2016) [OpenAIRE]

[11] Lam, D., Kuzma, R., McGee, K., Dooley, S., Laielli, M., Klaric, M., Bulatov, Y., McCord, B.: xview: Objects in context in overhead imagery. CoRR abs/1802.07856 (2018) [OpenAIRE]

[12] CosmiQWorks: Spacenet challenge utilites. https://github.com/SpaceNetChallenge/ utilities/ (2018)

[13] Christie, G., Fendley, N., Wilson, J., Mukherjee, R.: Functional map of the world. In: CVPR. (2018)

[14] Goldberg, H., Brown, M., Wang, S.: A benchmark for building footprint classification using orthorectified rgb imagery and digital surface models from commercial satellites. In: Proceedings of IEEE Applied Imagery Pattern Recognition Workshop 2017. (2017)

[15] CosmiQWorks: Apls metric. https://github.com/CosmiQ/apls (2017)

25 references, page 1 of 2
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