[7] Bruno Menini Matosak, Leila Maria Garcia Fonseca, Evandro Carrijo Taquary, Raian Vargas Maretto, Hugo do Nascimento Bendini, and Marcos Adami. Mapping Deforestation in Cerrado Based on Hybrid Deep Learning Architecture and Medium Spatial Resolution Satellite Time Series. Remote Sensing, 2022.
[8] Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe, Brian Barker, Blake Munshell, Madhava Paliyam, and Mehdi Hosseini. Rapid Response Crop Maps in Data Sparse Regions. CoRR, abs/2006.16866, 2020.
[9] Nataliia Kussul, Mykola Lavreniuk, Sergii Skakun, and Andrii Shelestov. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, 2017.
[10] Foyez Ahmed Prodhan, Jiahua Zhang, Fengmei Yao, Lamei Shi, Til Prasad Pangali Sharma, Da Zhang, Dan Cao, Minxuan Zheng, Naveed Ahmed, and Hasiba Pervin Mohana. Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data. Remote Sensing, 2021.
[11] R. Bentivoglio, E. Isufi, S. N. Jonkman, and R. Taormina. Deep learning methods for flood mapping: a review of existing applications and future research directions. Hydrology and Earth System Sciences, 2022.
[12] Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar, Debvrat Varshney, Masoud Yari, and Robin Roberson Murphy. FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding. IEEE Access, 2021.
[13] Fantine Huot, R. Lily Hu, Nita Goyal, Tharun Sankar, Matthias Ihme, and Yi-Fan Chen. Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 2022.
[14] Dmitry Rashkovetsky, Florian Mauracher, Martin Langer, and Michael Schmitt. Wildfire Detection From Multisensor Satellite Imagery Using Deep Semantic Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021.
[15] Panagiotis Barmpoutis, Periklis Papaioannou, Kosmas Dimitropoulos, and Nikos Grammalidis. A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing. Sensors, 2020.
[16] Arman Khachiyan, Anthony Thomas, Huye Zhou, Gordon Hanson, Alex Cloninger, Tajana Rosing, and Amit K. Khandelwal. Using Neural Networks to Predict Microspatial Economic Growth. American Economic Review: Insights, 2022.
[17] Esra Suel, Samir Bhatt, Michael Brauer, Seth Flaxman, and Majid Ezzati. Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas. Remote Sensing of Environment, 2021.
[18] Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications, 2020.
[19] Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon. Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. In AAAI, 2016.
[20] Luna Yue Huang, Solomon M Hsiang, and Marco Gonzalez-Navarro. Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs. Working paper, National Bureau of Economic Research, 2021.
[21] David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Sasha Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mikkavilli, Konrad P. Kording, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, and Yoshua Bengio. Tackling Climate Change with Machine Learning. ACM Comput. Surv., 2022.