Functional Map of the World
Open accessAbstract
We present a new dataset, Functional Map of the World (fMoW), which aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features. The metadata provided with each image enables reasoning about location, time, sun angles, physical sizes, and other features when making predictions about objects in the image. Our dataset consists of over 1 million images from over 200 countries. For each image, we provide at least one bounding box annotation containing one of 63 categories, including a "false detection" category. We present an analysis of the dataset along with baseline approaches that reason about metadata and temporal views. Our data, code, and pretrained models have been made publicly available.
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fMoW RGB is a dataset of high-resolution satellite image time series across the world, with a task of classification among 62 categories.
“Functional Map of the World (fMoW) [17] is a dataset of high-resolution satellite image time series across the world, with a task of classification among 62 categories.”
From SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery (2022) · cites this paper as [17]
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Matterport3D: Learning from RGB-D Data in Indoor Environments
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niebner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00081 · Cited by 1,858 · see citing works
TorontoCity: Seeing the World with a Million Eyes
Wang, S., Bai, M., Mattyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., & Urtasun, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.327 · Cited by 186 · see citing works
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Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046 · see citing works
Robust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00780 · Cited by 428 · see citing works
RingMo: A Remote Sensing Foundation Model With Masked Image Modeling
Sun, X., Wang, P., Lu, W., Zhu, Z., Lu, X., He, Q., Li, J., Rong, X., Yang, Z., Chang, H., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3194732 · Cited by 323 · see citing works
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.05482 · Cited by 307 · see citing works
SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery
Cong, Y., Khanna, S., Meng, C., Liu, P., Rozi, E., He, Y., Burke, M., Lobell, D., & Ermon, S. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-0015 · Cited by 303 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
“We study this problem through a variant of the Functional Map of the World dataset (Christie et al., 2018).”
InfluentialOn Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250 · see citing works
SpaceNet MVOI: A Multi-View Overhead Imagery Dataset
Weir, N., Lindenbaum, D., Bastidas, A., Etten, A., Kumar, V., Mcpherson, S., Shermeyer, J., & Tang, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00108 · Cited by 95 · see citing works
Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv · doi:10.48550/arxiv.2107.04649 · Cited by 74 · see citing works
- Authors
- Christie, G., Fendley, N., Wilson, J., & Mukherjee, R.
- Affiliations
- Johns Hopkins University Applied Physics Laboratory (US)
- ORCID
- Christie, Gordon
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 6172-6180
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2768404247
- Cited by
- 312
- References
- 5
- Citations by year
- 2026: 40 · 2025: 80 · 2024: 54 · 2023: 32 · 2022: 25 · 2021: 39 · 2020: 21 · 2019: 8 · 2018: 12 · 2017: 1
- FWCI
- 39.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Keywords
- land use classification, satellite imagery, temporal image sequences, image metadata, object detection, bounding box annotation, false detection
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