Fetching the paper…
Reading the bibliography…
Satellite data has the potential to inspire a seismic shift for machine learning -- one in which we rethink existing practices designed for traditional data modalities.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Machine learning that matters
Wagstaff, K. L · 2012
Earlier work this paper cites.
The role of satellite remote sensing in climate change studies
Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., and Dickinson, R · 2013
Earlier work this paper cites.
Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation
Le Rest, K., Pinaud, D., Monestiez, P., Chadoeuf, J., and Bretagnolle, V · 2014
Earlier work this paper cites.
Good practices for estimating area and assessing accuracy of land change
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A · 2014
Earlier work this paper cites.
DETER-B: The new Amazon near real-time deforestation detection system
Diniz, C. G., de Almeida Souza, A. A., Santos, D. C., Dias, M. C., Da Luz, N. C., De Moraes, D. R. V., Maia, J. S., Gomes, A. R., da Silva Narvaes, I., Valeriano, D. M., et al · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Combining satellite imagery and machine learning to predict poverty
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S · 2016
Earlier work this paper cites.
Domain adaptation for the classification of remote sensing data: An overview of recent advances
Tuia, D., Persello, C., and Bruzzone, L · 2016
Earlier work this paper cites.
Getting ready for nisar—and for managing big data using the commercial cloud
Blumenfeld, J · 2017
Earlier work this paper cites.
SoilGrids250m: Global gridded soil information based on machine learning
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al · 2017
Earlier work this paper cites.
Toward seamless multiview scene analysis from satellite to street level
Lefevre, S., Tuia, D., Wegner, J. D., Produit, T., and Nassar, A. S · 2017
Earlier work this paper cites.
Estimating the prediction performance of spatial models via spatial k-fold cross validation
Pohjankukka, J., Pahikkala, T., Nevalainen, P., and Heikkonen, J · 2017
Earlier work this paper cites.
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., et al · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Predicting ground-level scene layout from aerial imagery
Zhai, M., Bessinger, Z., Workman, S., and Jacobs, N · 2017
Earlier work this paper cites.
Deep learning in remote sensing: A comprehensive review and list of resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F · 2017
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H · 2018
Earlier work this paper cites.
Functional map of the world
Christie, G., Fendley, N., Wilson, J., and Mukherjee, R · 2018
Earlier work this paper cites.
Evaluation of using Sentinel-1 and-2 time-series to identify winter land use in agricultural landscapes
Denize, J., Hubert-Moy, L., Betbeder, J., Corgne, S., Baudry, J., and Pottier, E · 2018
Earlier work this paper cites.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Earlier work this paper cites.
CVM-Net: Cross-view matching network for image-based ground-to-aerial geo-localization
Hu, S., Feng, M., Nguyen, R. M., and Lee, G. H · 2018
Earlier work this paper cites.
Tiling and stitching segmentation output for remote sensing: Basic challenges and recommendations
Huang, B., Reichman, D., Collins, L. M., Bradbury, K., and Malof, J. M · 2018
Earlier work this paper cites.
Land cover mapping at very high resolution with rotation equivariant cnns: Towards small yet accurate models
Marcos, D., Volpi, M., Kellenberger, B., and Tuia, D · 2018
Earlier work this paper cites.
Combining sentinel-1 and sentinel-2 data for improved land use and land cover mapping of monsoon regions
Steinhausen, M. J., Wagner, P. D., Narasimhan, B., and Waske, B · 2018
Earlier work this paper cites.
You only look twice: Rapid multi-scale object detection in satellite imagery
Van Etten, A · 2018
Earlier work this paper cites.
Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: A case study for Belgium
Van Tricht, K., Gobin, A., Gilliams, S., and Piccard, I · 2018
Earlier work this paper cites.
A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms
Whyte, A., Ferentinos, K. P., and Petropoulos, G. P · 2018
Earlier work this paper cites.
Openmapflow: a library for rapid map creation with machine learning and remote sensing data
Zvonkov, I., Tseng, G., Nakalembe, C., and Kerner, H · 2018
Earlier work this paper cites.
The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers
Airola, A., Pohjankukka, J., Torppa, J., Middleton, M., Nykänen, V., Heikkonen, J., and Pahikkala, T · 2019
Earlier work this paper cites.
Machine learning for data-driven discovery in solid earth geoscience
Bergen, K. J., Johnson, P. A., de Hoop, M. V., and Beroza, G. C · 2019
Earlier work this paper cites.
Tile2vec: Unsupervised representation learning for spatially distributed data
Jean, N., Wang, S., Samar, A., Azzari, G., Lobell, D., and Ermon, S · 2019
Earlier work this paper cites.
Presence-only geographical priors for fine-grained image classification
Mac Aodha, O., Cole, E., and Perona, P · 2019
Earlier work this paper cites.
Model cards for model reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., and Gebru, T · 2019
Earlier work this paper cites.
Deep learning and process understanding for data-driven Earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat, f · 2019
Earlier work this paper cites.
Large scale high-resolution land cover mapping with multi-resolution data
Robinson, C., Hou, L., Malkin, K., Soobitsky, R., Czawlytko, J., Dilkina, B., and Jojic, N · 2019
Earlier work this paper cites.
Sen12ms – a curated dataset of georeferenced multi-spectral sentinel-1/2 imagery for deep learning and data fusion
Schmitt, M., Hughes, L. H., Qiu, C., and Zhu, X. X · 2019
Earlier work this paper cites.
Domain adaptation for convolutional neural networks-based remote sensing scene classification
Song, S., Yu, H., Miao, Z., Zhang, Q., Lin, Y., and Wang, S · 2019
Earlier work this paper cites.
Key issues in rigorous accuracy assessment of land cover products
Stehman, S. V. and Foody, G. M · 2019
Cited alongside, same era.
Bigearthnet: A large-scale benchmark archive for remote sensing image understanding
Sumbul, G., Charfuelan, M., Demir, B., and Markl, V · 2019
Cited alongside, same era.
Learning to interpret satellite images in global scale using Wikipedia
Uzkent, B., Sheehan, E., Meng, C., Tang, Z., Burke, M., Lobell, D., and Ermon, S · 2019
Cited alongside, same era.
Building damage detection in satellite imagery using convolutional neural networks
Xu, J. Z., Lu, W., Li, Z., Khaitan, P., and Zaytseva, V · 2019
Cited alongside, same era.
Assessing out-of-domain generalization for robust building damage detection
Benson, V. and Ecker, A · 2020
Cited alongside, same era.
Spate-gan: Improved generative modeling of dynamic spatio-temporal patterns with an autoregressive embedding loss
Klemmer, K., Xu, T., Acciaio, B., and Neill, D. B · 2022
Later among the works it cites.
Earth observation and artificial intelligence: Understanding emerging ethical issues and opportunities
Kochupillai, M., Kahl, M., Schmitt, M., Taubenböck, H., and Zhu, X. X · 2022
Later among the works it cites.
Deep learning in multimodal remote sensing data fusion: A comprehensive review
Li, J., Hong, D., Gao, L., Yao, J., Zheng, K., Zhang, B., and Chanussot, J · 2022
Later among the works it cites.
Machine learning-based global maps of ecological variables and the challenge of assessing them
Meyer, H. and Pebesma, E · 2022
Later among the works it cites.
Nearest neighbour distance matching leave-one-out cross-validation for map validation
Milà, C., Mateu, J., Pebesma, E., and Meyer, H · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A guide for collecting and sharing ground reference data for machine learning applications
Bromberg Gaber, Y · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
The care principles for indigenous data governance
Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., et al · 2020
Cited alongside, same era.
Satellite image time series classification with pixel-set encoders and temporal self-attention
Garnot, V. S. F., Landrieu, L., Giordano, S., and Chehata, N · 2020
Cited alongside, same era.
Towards delivering on the sustainable development goals using Earth observations
Kavvada, A., Metternicht, G., Kerblat, F., Mudau, N., Haldorson, M., Laldaparsad, S., Friedl, L., Held, A., and Chuvieco, E · 2020
Cited alongside, same era.
Multi-scale representation learning for spatial feature distributions using grid cells
Mai, G., Janowicz, K., Yan, B., Zhu, R., Cai, L., and Lao, N · 2020
Cited alongside, same era.
Advancing AI for Earth science: A data systems perspective
Maskey, M., Alemohammad, H., Murphy, K., and Ramachandran, R · 2020
Cited alongside, same era.
An artificial intelligence dataset for solar energy locations in India
Ortiz, A., Negandhi, D., Mysorekar, S. R., Nagaraju, S. K., Kiesecker, J., Robinson, C., Bhatia, P., Khurana, A., Wang, J., Oviedo, F., et al · 2022
Later among the works it cites.
Scale-MAE: A scale-aware masked autoencoder for multiscale geospatial representation learning
Reed, C. J., Gupta, R., Li, S., Brockman, S., Funk, C., Clipp, B., Candido, S., Uyttendaele, M., and Darrell, T · 2022
Later among the works it cites.
Resolving label uncertainty with implicit posterior models
Rolf, E., Malkin, N., Graikos, A., Jojic, A., Robinson, C., and Jojic, N · 2022
Later among the works it cites.
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al · 2022
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al · 2022
Later among the works it cites.
Timl: Task-informed meta-learning for crop type mapping
Tseng, G., Kerner, H., and Rolnick, D · 2022
Later among the works it cites.
An empirical study of remote sensing pretraining
Wang, D., Zhang, J., Du, B., Xia, G.-S., and Tao, D · 2022
Later among the works it cites.
Predicting out-of-distribution error with the projection norm
Yu, Y., Yang, Z., Wei, A., Ma, Y., and Steinhardt, J · 2022
Later among the works it cites.
Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy
Aiken, E., Rolf, E., and Blumenstock, J · 2023
Later among the works it cites.
SatlasPretrain: A large-scale dataset for remote sensing image understanding
Bastani, F., Wolters, P., Gupta, R., Ferdinando, J., and Kembhavi, A · 2023
Later among the works it cites.
Cepeda, V. V., Nayak, G. K., and Shah, M · 2023
Later among the works it cites.
Revisiting pre-trained remote sensing model benchmarks: Resizing and normalization matters
Corley, I., Robinson, C., Dodhia, R., Ferres, J. M. L., and Najafirad, P · 2023
Later among the works it cites.
Sat2Cap: Mapping fine-grained textual descriptions from satellite images
Dhakal, A., Ahmad, A., Khanal, S., Sastry, S., and Jacobs, N · 2023
Later among the works it cites.
“Do you collect data to give to the university or do you do the work to benefit people?”: Indigenous data sovereignty in environmental contexts
Dogan, A. L. and Wood, D · 2023
Later among the works it cites.
CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders
Fuller, A., Millard, K., and Green, J. R · 2023
Later among the works it cites.
Multi-region transfer learning for segmentation of crop field boundaries in satellite images with limited labels
Kerner, H., Sundar, S., and Satish, M · 2023
Later among the works it cites.
Learning tri-modal embeddings for zero-shot soundscape mapping
Khanal, S., Sastry, S., Dhakal, A., and Jacobs, N · 2023
Later among the works it cites.
SatCLIP: Global, general-purpose location embeddings with satellite imagery
Klemmer, K., Rolf, E., Robinson, C., Mackey, L., and Rußwurm, M · 2023
Later among the works it cites.
Geo-bench: Toward foundation models for earth monitoring
Lacoste, A., Lehmann, N., Rodriguez, P., Sherwin, E. D., Kerner, H., Lütjens, B., Irvin, J. A., Dao, D., Alemohammad, H., Drouin, A., et al · 2023
Later among the works it cites.
CSP: Self-supervised contrastive spatial pre-training for geospatial-visual representations
Mai, G., Lao, N., He, Y., Song, J., and Ermon, S · 2023
Later among the works it cites.
Considerations for AI-EO for agriculture in Sub-Saharan Africa
Nakalembe, C. and Kerner, H · 2023
Later among the works it cites.
Satellite remote sensing for environmental data justice: Perspectives from anti-prison community organizers on the uses of geospatial data
Ovienmhada, U., Diongue, A., Pellow, D. N., and Wood, D · 2023
Later among the works it cites.
Evaluation challenges for geospatial ML
Rolf, E · 2023
Later among the works it cites.
Data-centric machine learning for geospatial remote sensing data
Roscher, R., Rußwurm, M., Gevaert, C., Kampffmeyer, M., Santos, J. A. d., Vakalopoulou, M., Hänsch, R., Hansen, S., Nogueira, K., Prexl, J., et al · 2023
Later among the works it cites.
Ssl4eo-l: Datasets and foundation models for landsat imagery
Stewart, A. J., Lehmann, N., Corley, I. A., Wang, Y., Chang, Y.-C., Braham, N. A. A., Sehgal, S., Robinson, C., and Banerjee, A · 2023
Later among the works it cites.
Lightweight, pre-trained transformers for remote sensing timeseries, 2023
Tseng, G., Zvonkov, I., Purohit, M., Rolnick, D., and Kerner, H · 2023
Later among the works it cites.
Artificial intelligence to advance Earth observation: a perspective
Tuia, D., Schindler, K., Demir, B., Camps-Valls, G., Zhu, X. X., Kochupillai, M., Džeroski, S., van Rijn, J. N., Hoos, H. H., Del Frate, F., et al · 2023
Later among the works it cites.
Mapping crops at global scale! What works and what doesn’t?
Van Tricht, K · 2023
Later among the works it cites.
Model evaluation for geospatial problems
Wang, J., Hallman, T., Hopkins, L., Kilbride, J. B., Robinson, W. D., and Hutchinson, R · 2023
Later among the works it cites.
Ssl4eo-s12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [software and data sets]
Wang, Y., Braham, N. A. A., Xiong, Z., Liu, C., Albrecht, C. M., and Zhu, X. X · 2023
Later among the works it cites.
Copernicus Data Space Dashboard
Copernicus · 2024
Closest in time.
Exploring masked autoencoders for sensor-agnostic image retrieval in remote sensing, 2024
Hackstein, J., Sumbul, G., Clasen, K. N., and Demir, B · 2024
Closest in time.
Meta-learning to address diverse Earth observation problems across resolutions
Rußwurm, M., Wang, S., Kellenberger, B., Roscher, R., and Tuia, D · 2024
Closest in time.
Geographic location encoding with spherical harmonics and sinusoidal representation networks
Rußwurm, M., Klemmer, K., Rolf, E., Zbinden, R., and Tuia, D · 2024
Closest in time.