Description: This repository contains a directory of papers on earth observation, causal inference, machine learning, and/or poverty research compiled by Kaz Sakamoto for a scoping literature review in collaboration with Adel Daoud and Connor T. Jerzak. To find associated .bib entries, see eo-poverty-review/citations.
Contribute: You can additional papers for inclusion by opening an issue in this repo (feel free to suggest your own work!).
Overview | Literature Directory | Reference
Ali, Sahara, Omar Faruque, Yiyi Huang, Md. Osman Gani, Aneesh Subramanian, Nicole-Jeanne Schlegel, and Jianwu Wang. 2023. “Quantifying Causes of Arctic Amplification via Deep Learning Based Time-Series Causal Inference.” In 2023 International Conference on Machine Learning and Applications (ICMLA), 689–96. https://doi.org/10.1109/ICMLA58977.2023.00101.
Berrevoets, Jeroen, Krzysztof Kacprzyk, Zhaozhi Qian, and Mihaela van der Schaar. 2024. “Causal Deep Learning.” arXiv. http://arxiv.org/abs/2303.02186.
Biswas, Mriganka Sekhar, and Manmeet Singh. 2022. “Trustworthy Modelling of Atmospheric Formaldehyde Powered by Deep Learning.” arXiv. http://arxiv.org/abs/2209.07414.
Boussard, Julien, Chandni Nagda, Julia Kaltenborn, Charlotte Emilie Elektra Lange, Philippe Brouillard, Yaniv Gurwicz, Peer Nowack, and David Rolnick. 2023. “Towards Causal Representations of Climate Model Data.” arXiv. http://arxiv.org/abs/2312.02858.
Camps-Valls, G. 2021. “Perspective on Deep Learning for Earth Sciences.” In Generalization With Deep Learning: For Improvement On Sensing Capability, 159–73. https://doi.org/10.1142/9789811218842_0007.
Camps-Valls, G., M. Reichstein, X. Zhu, and D. Tuia. 2020. “ADVANCING DEEP LEARNING for EARTH SCIENCES: From HYBRID MODELING to INTERPRETABILITY.” In, 3979–82. https://doi.org/10.1109/IGARSS39084.2020.9323558.
Camps-Valls, Gustau, Daniel H. Svendsen, Jordi Cortés-Andrés, Álvaro Mareno-Martínez, Adrián Pérez-Suay, Jose Adsuara, Irene Martín, Maria Piles, Jordi Muñoz-Marí, and Luca Martino. 2021. “Physics-Aware Machine Learning for Geosciences and Remote Sensing.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2086–89. https://doi.org/10.1109/IGARSS47720.2021.9554521.
Chen, Xiangyu, Kaisa Zhang, Gang Chuai, Weidong Gao, Zhiwei Si, Yijian Hou, and Xuewen Liu. 2023. “Urban Area Characterization and Structure Analysis: A Combined Data-Driven Approach by Remote Sensing Information and Spatial-Temporal Wireless Data.” REMOTE SENSING 15 (4): 1041. https://doi.org/10.3390/rs15041041.
Chouzenoux, Emilie, and Victor Elvira. 2023. “Sparse Graphical Linear Dynamical Systems.” arXiv. http://arxiv.org/abs/2307.03210.
Cohrs, Kai-Hendrik, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, and Gustau Camps-Valls. 2024. “Causal Hybrid Modeling with Double Machine Learning.” arXiv. http://arxiv.org/abs/2402.13332.
Das, M., and S. K. Ghosh. 2017. “A Deep-Learning-Based Forecasting Ensemble to Predict Missing Data for Remote Sensing Analysis.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (12): 5228–36. https://doi.org/10.1109/JSTARS.2017.2760202.
Debeire, Kevin, Jakob Runge, Andreas Gerhardus, and Veronika Eyring. 2024. “Bootstrap Aggregation and Confidence Measures to Improve Time Series Causal Discovery.” arXiv. http://arxiv.org/abs/2306.08946.
Dong, Hongwei, Lingyu Si, Wenwen Qiang, Lamei Zhang, Junzhi Yu, Yuquan Wu, Changwen Zheng, and Fuchun Sun. 2024. “A Novel Causal Inference-Guided Feature Enhancement Framework for PolSAR Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 62: 1–16. https://doi.org/10.1109/TGRS.2023.3343380.
Eldhose, Elizabeth, Tejasvi Chauhan, Vikram Chandel, Subimal Ghosh, and Auroop R Ganguly. n.d. “Robust Causality and False Attribution in Data-Driven Earth Science Discoveries.”
Elvira, Víctor, Émilie Chouzenoux, Jordi Cerdà, and Gustau Camps-Valls. 2023. “Graphs in State-Space Models for Granger Causality in Climate Science.” arXiv. http://arxiv.org/abs/2307.10703.
Fernández-Loría, Carlos, and Jorge Loría. 2024. “Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification.” arXiv. http://arxiv.org/abs/2206.12532.
Giannarakis, Georgios, Ilias Tsoumas, Stelios Neophytides, Christiana Papoutsa, Charalampos Kontoes, and Diofantos Hadjimitsis. 2023. “Understanding the Impacts of Crop Diversification in the Context of Climate Change: A Machine Learning Approach.” arXiv. http://arxiv.org/abs/2307.08617.
Giannarakis, Georgios, Vasileios Sitokonstantinou, Roxanne Suzette Lorilla, and Charalampos Kontoes. 2022. “Towards Assessing Agricultural Land Suitability with Causal Machine Learning.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1441–51. https://doi.org/10.1109/CVPRW56347.2022.00150.
Giannarakis, G., I. Tsoumas, S. Neophytides, C. Papoutsa, C. Kontoes, and D. Hadjimitsis. 2023. “UNDERSTANDING THE IMPACTS OF CROP DIVERSIFICATION IN THE CONTEXT OF CLIMATE CHANGE: A MACHINE LEARNING APPROACH.” In, 48:1379–84. https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1379-2023.
Go, Eugenia, Kentaro Nakajima, Yasuyuki Sawada, and Kiyoshi Taniguchi. 2022. “On the Use of Satellite-Based Vehicle Flows Data to Assess Local Economic Activity: The Case of Philippine Cities.” {SSRN} {Scholarly} {Paper}. Rochester, NY. https://doi.org/10.2139/ssrn.4057690.
Gomez-Chova, Luis, Raul Santos-Rodriguez, and Gustau Camps-Valls. 2018. “Signal-to-Noise Ratio in Reproducing Kernel Hilbert Spaces.” PATTERN RECOGNITION LETTERS 112 (September): 75–82. https://doi.org/10.1016/j.patrec.2018.06.004.
Gómez-Chova, Luis, and Gustavo Camps-Valls. 2012. “Learning with the Kernel Signal to Noise Ratio.” In 2012 IEEE International Workshop on Machine Learning for Signal Processing, 1–6. https://doi.org/10.1109/MLSP.2012.6349715.
Harding, M. C., and C. Lamarche. 2021. “Small Steps with Big Data: Using Machine Learning in Energy and Environmental Economics.” Annual Review of Resource Economics 13: 469–88. https://doi.org/10.1146/annurev-resource-100920-034117.
Heuer, Helge, Mierk Schwabe, Pierre Gentine, Marco A. Giorgetta, and Veronika Eyring. 2023. “Interpretable Multiscale Machine Learning-Based Parameterizations of Convection for ICON.” arXiv. http://arxiv.org/abs/2311.03251.
Jay, Jonathan. 2020. “Alcohol Outlets and Firearm Violence: A Place-Based Case-Control Study Using Satellite Imagery and Machine Learning.” INJURY PREVENTION 26 (1): 61–66. https://doi.org/10.1136/injuryprev-2019-043248.
Jerzak, C. T., F. Johansson, and A. Daoud. 2023. “Image-Based Treatment Effect Heterogeneity.” Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR), 213: 531-552. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147679914&partnerID=40&md5=7866b08ec210422443d7ad823941845f.
Jerzak, Connor T., and Adel Daoud. 2023. “CausalImages: An R Package for Causal Inference with Earth Observation, Bio-Medical, and Social Science Images.” arXiv. http://arxiv.org/abs/2310.00233.
Jerzak, Connor T., Fredrik Johansson, and Adel Daoud. 2023a. “Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities.” arXiv. http://arxiv.org/abs/2301.12985.
Jerzak, Connor T., Fredrik Johansson, and Adel Daoud. 2023b. “Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa.” arXiv. http://arxiv.org/abs/2206.06410.
Ji, J., T. Wang, J. Liu, M. Wang, and W. Tang. 2024. “River Runoff Causal Discovery with Deep Reinforcement Learning.” Applied Intelligence 54 (4): 3547–65. https://doi.org/10.1007/s10489-024-05348-7.
Li, Hongga, Xiaoxia Huang, and Xia Li. 2019. “Urban Land Price Assessment Based on GIS and Deep Learning.” In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 935–38. https://doi.org/10.1109/IGARSS.2019.8900516.
Li, L. U., W. E. I. Shangguan, Y. I. Deng, J. Mao, J. Pan, N. A. N. Wei, H. U. A. Yuan, S. Zhang, Y. Zhang, and Y. Dai. 2020. “A Causal Inference Model Based on Random Forests to Identify the Effect of Soil Moisture on Precipitation.” Journal of Hydrometeorology 21 (5): 1115–31. https://doi.org/10.1175/JHM-D-19-0209.1.
Li, X., M. Feng, Y. Ran, Y. Su, F. Liu, C. Huang, H. Shen, et al. 2023. “Big Data in Earth System Science and Progress Towards a Digital Twin.” Nature Reviews Earth and Environment. https://doi.org/10.1038/s43017-023-00409-w.
Liang, X. San, X San Liang, Dake Chen, and Renhe Zhang. 2024. “Quantitative Causality, Causality-Guided Scientific Discovery, and Causal Machine Learning.” https://doi.org/10.22541/essoar.170913638.81842156/v1.
Lin, L., L. Di, C. Zhang, L. Guo, H. Zhao, D. Islam, H. Li, Z. Liu, and G. Middleton. 2024. “Modeling Urban Redevelopment: A Novel Approach Using Time-Series Remote Sensing Data and Machine Learning.” Geography and Sustainability 5 (2): 211–19. https://doi.org/10.1016/j.geosus.2024.02.001.
Luo, J., and Q. Zhang. 2019. “Big Data Pioneers New Ways of Geoscience Research: Identifying Relevant Relationships to Enhance Research Feasibility.” Earth Science Frontiers 26 (4): 6–12. https://doi.org/10.13745/j.esf.sf.2019.4.28.
Ma, Qiaoyu, Yang Liu, Xintong Wang, Biao Yuan, and Kai Zhang. 2021. “Hyperspectral Image Recognition Based on Lightweight Causal Convolutional Network.” In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 431–35. https://doi.org/10.1109/ICBAIE52039.2021.9389851.
Mateo-Sanchis, Anna, Jordi Muñoz-Marí, Adrián Pérez-Suay, and Gustau Camps-Valls. 2020. “Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference.” arXiv. http://arxiv.org/abs/2012.12105.
Moe, S. Jannicke, John F. Carriger, and Miriam Glendell. 2021. “Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments.” INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 17 (1): 53–61. https://doi.org/10.1002/ieam.4369.
Morata-Dolz, Miguel, Diego Bueso, Maria Piles, and Gustau Camps-Valls. 2020. “Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality.” arXiv. http://arxiv.org/abs/2012.03338.
Mu, Bin, Bo Qin, Shijin Yuan, Xin Wang, and Yuxuan Chen. 2023. “PIRT: A Physics-Informed Red Tide Deep Learning Forecast Model Considering Causal-Inferred Predictors Selection.” IEEE Geoscience and Remote Sensing Letters 20: 1–5. https://doi.org/10.1109/LGRS.2023.3250642.
Noy, K., N. Ohana-Levi, N. Panov, M. Silver, and A. Karnieli. 2021. “A Long-Term Spatiotemporal Analysis of Biocrusts Across a Diverse Arid Environment: The Case of the Israeli-Egyptian Sandfield.” Science of the Total Environment 774. https://doi.org/10.1016/j.scitotenv.2021.145154.
Otgonbaatar, Soronzonbold, Mihai Datcu, and Begüm Demir. 2022. “Causality for Remote Sensing: An Exploratory Study.” In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 259–62. https://doi.org/10.1109/IGARSS46834.2022.9883060.
Pérez-Suay, Adrián, and Gustau Camps-Valls. 2019. “Causal Inference in Geoscience and Remote Sensing From Observational Data.” IEEE Transactions on Geoscience and Remote Sensing 57 (3): 1502–13. https://doi.org/10.1109/TGRS.2018.2867002.
Qiu, Xinzhu, Yunzhe Wang, Jingyi Cao, Guannan Xu, Yanan You, and Junlong Ren. 2021. “Economic Development Analysis of the Belt and Road Regions Based on Automatic Interpretation of Remote Sensing Images.” In 2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), 96–101. https://doi.org/10.1109/IC-NIDC54101.2021.9660561.
Ramachandra, Vikas. n.d. “Causal Inference for Climate Change Events from Satellite Image Time Series Using Computer Vision and Deep Learning.”
Ratledge, Nathan, Gabe Cadamuro, Brandon de la Cuesta, Matthieu Stigler, and Marshall Burke. 2022. “Using Machine Learning to Assess the Livelihood Impact of Electricity Access.” NATURE 611 (7936): 491–+. https://doi.org/10.1038/s41586-022-05322-8.
Rong, Yineng, and X. San Liang. 2022. “An Information Flow-Based Sea Surface Height Reconstruction Through Machine Learning.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–9. https://doi.org/10.1109/TGRS.2022.3140398.
Runge, Jakob, Andreas Gerhardus, Gherardo Varando, Veronika Eyring, and Gustau Camps-Valls. 2023. “Causal Inference for Time Series.” NATURE REVIEWS EARTH & ENVIRONMENT 4 (7): 487–505. https://doi.org/10.1038/s43017-023-00431-y.
Runge, J., X.-A. Tibau, M. Bruhns, J. Muñoz-Marí, and G. Camps-Valls. 2019. “The Causality for Climate Competition.” In, 123:110–20. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102541381&partnerID=40&md5=12adb7bf6e4901f53374a3d818bf7f19.
Sarkodie, S. A. 2020. “Causal Effect of Environmental Factors, Economic Indicators and Domestic Material Consumption Using Frequency Domain Causality Test.” Science of the Total Environment 736. https://doi.org/10.1016/j.scitotenv.2020.139602.
Seong, Nohyoon. 2021. “Deep Spatiotemporal Attention Network for Fine Particle Matter 2.5 Concentration Prediction With Causality Analysis.” IEEE Access 9: 73230–39. https://doi.org/10.1109/ACCESS.2021.3080828.
Serdavaa, Batkhurel. 2023. “A Satellite Image Analysis on Housing Conditions and the Effectiveness of the Affordable Housing Mortgage Program in Mongolia: A Deep Learning Approach.” {SSRN} {Scholarly} {Paper}. Rochester, NY. https://doi.org/10.2139/ssrn.4664966.
Sharma, Somya, Swati Sharma, Rafael Padilha, Emre Kiciman, and Ranveer Chandra. 2024. “Domain Adaptation for Sustainable Soil Management Using Causal and Contrastive Constraint Minimization.” arXiv. http://arxiv.org/abs/2401.07175.
Su, J., D. Chen, D. Zheng, Y. Su, and X. Li. 2023. “The Insight of Why: Causal Inference in Earth System Science.” Science China Earth Sciences 66 (10): 2169–86. https://doi.org/10.1007/s11430-023-1148-7.
Tan, S.-C., G.-Y. Shi, J.-H. Shi, H.-W. Gao, and X. Yao. 2011. “Correlation of Asian Dust with Chlorophyll and Primary Productivity in the Coastal Seas of China During the Period from 1998 to 2008.” Journal of Geophysical Research: Biogeosciences 116 (2). https://doi.org/10.1029/2010JG001456.
Tesch, T., S. Kollet, and J. Garcke. 2023. “Causal Deep Learning Models for Studying the Earth System.” Geoscientific Model Development 16 (8): 2149–66. https://doi.org/10.5194/gmd-16-2149-2023.
Tesch, Tobias, Stefan Kollet, and Jochen Garcke. 2021. “Variant Approach for Identifying Spurious Relations That Deep Learning Models Learn.” FRONTIERS IN WATER 3 (September): 745563. https://doi.org/10.3389/frwa.2021.745563.
Tuia, Devis, Ribana Roscher, Jan Dirk Wegner, Nathan Jacobs, Xiaoxiang Zhu, and Gustau Camps-Valls. 2021. “Toward a Collective Agenda on AI for Earth Science Data Analysis.” IEEE Geoscience and Remote Sensing Magazine 9 (2): 88–104. https://doi.org/10.1109/MGRS.2020.3043504.
Wang, Chenguang, Yepeng Liu, Xiaojian Zhang, Xuechun Li, Vladimir Paramygin, Arthriya Subgranon, Peter Sheng, Xilei Zhao, and Susu Xu. 2023. “Causality-Informed Rapid Post-Hurricane Building Damage Detection in Large Scale from InSAR Imagery.” arXiv. http://arxiv.org/abs/2310.01565.
Wang, Guangxing, Guoshuai Dong, Hui Li, Lirong Han, Xuanwen Tao, and Peng Ren. 2019. “Remote Sensing Image Synthesis via Graphical Generative Adversarial Networks.” In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 10027–30. https://doi.org/10.1109/IGARSS.2019.8898915.
Wang, Jun, and Klaus Mueller. 2016. “The Visual Causality Analyst: An Interactive Interface for Causal Reasoning.” IEEE Transactions on Visualization and Computer Graphics 22 (1): 230–39. https://doi.org/10.1109/TVCG.2015.2467931.
Wang, Yu-Hsiang, You Cartus Bo-Xiang, Hsiung-Ming Liao, Ming-Ching Chang, and Richard Tsai. 2024. “Deeppvmap: Deep Photovoltaic Map for Efficient Segmentation of Solar Panels from Low-Resolution Aerial Imagery.” {SSRN} {Scholarly} {Paper}. Rochester, NY. https://doi.org/10.2139/ssrn.4779346.
Wei, C.-C. 2014. “Meta-Heuristic Bayesian Networks Retrieval Combined Polarization Corrected Temperature and Scattering Index for Precipitations.” Neurocomputing 136: 71–81. https://doi.org/10.1016/j.neucom.2014.01.030.
Weichwald, Sebastian, Martin E. Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, and Gherardo Varando. 2020. “Causal Structure Learning from Time Series: Large Regression Coefficients May Predict Causal Links Better in Practice Than Small p-Values.” arXiv. http://arxiv.org/abs/2002.09573.
Xiong, Wei, Zhenyu Xiong, and Yaqi Cui. 2022a. “A Confounder-Free Fusion Network for Aerial Image Scene Feature Representation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 5440–54. https://doi.org/10.1109/JSTARS.2022.3189052.
Xiong, Wei, Zhenyu Xiong, and Yaqi Cui. 2022b. “An Explainable Attention Network for Fine-Grained Ship Classification Using Remote-Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–14. https://doi.org/10.1109/TGRS.2022.3162195.
Xu, Fangcao, Jian Sun, Guido Cervone, and Mark Salvador. 2022. “Ill-Posed Surface Emissivity Retrieval from Multi-Geometry Hyperspectral Images Using a Hybrid Deep Neural Network.” arXiv. http://arxiv.org/abs/2107.04631.
Xu, Qingsong, Yilei Shi, Jonathan Bamber, Ye Tuo, Ralf Ludwig, and Xiao Xiang Zhu. 2024. “Physics-Aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-Based Hydrology.” arXiv. http://arxiv.org/abs/2310.05227.
Xu, Zhengyi, Wen Jiang, and Jie Geng. 2024. “Texture-Aware Causal Feature Extraction Network for Multimodal Remote Sensing Data Classification.” IEEE Transactions on Geoscience and Remote Sensing 62: 1–12. https://doi.org/10.1109/TGRS.2024.3368091.
Yu, Qiuyan, Wenjie Ji, Lara Prihodko, C. Wade Ross, Julius Y. Anchang, and Niall P. Hanan. 2021. “Study Becomes Insight: Ecological Learning from Machine Learning.” METHODS IN ECOLOGY AND EVOLUTION 12 (11): 2117–28. https://doi.org/10.1111/2041-210X.13686.
Yuan, K., Q. Zhu, W. J. Riley, F. Li, and H. Wu. 2022. “Understanding and Reducing the Uncertainties of Land Surface Energy Flux Partitioning Within CMIP6 Land Models.” Agricultural and Forest Meteorology 319. https://doi.org/10.1016/j.agrformet.2022.108920.
Zhang, Zhaoxiang, Yuelei Xu, Qi Cui, Qing Zhou, and Linhua Ma. 2022. “Unsupervised SAR and Optical Image Matching Using Siamese Domain Adaptation.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–16. https://doi.org/10.1109/TGRS.2022.3170316.
Zheng, Y., H. Zheng, and X. Ye. 2016. “Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China.” Sustainability (Switzerland) 8 (11). https://doi.org/10.3390/su8111124.
Zheng, Zhuo, Yanfei Zhong, Shiqi Tian, Ailong Ma, and Liangpei Zhang. 2022. “ChangeMask: Deep Multi-Task Encoder-Transformer-Decoder Architecture for Semantic Change Detection.” ISPRS Journal of Photogrammetry and Remote Sensing 183 (January): 228–39. https://doi.org/10.1016/j.isprsjprs.2021.10.015.
Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud. Planetary Causal Inference: Implications for the Geography of Poverty. ArXiv Preprint, 2024. [PDF]
@incollection{sakamoto2024scoping,
author = {Sakamoto, Kazuki and Jerzak, Connor T. and Daoud, Adel},
title = {A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty},
booktitle = {Geography of Poverty},
editor = {Hall, Ola and Wahab, Ibrahim},
year = {2025}
}
