mlsanityData to 7 Oct 2026

Towards a Collective Agenda on AI for Earth Science Data Analysis

arXiv 2104.05107 ↗First posted 11 Apr 2021cs.CV eess.SPcited by 27

Not on the map. Why?

Abstract (arXiv)

In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to advance the modeling and understanding of the Earth system. Despite such great opportunities, we also observed a worrying tendency to remain in disciplinary comfort zones applying recent advances from artificial intelligence on well resolved remote sensing problems. Here we take a position on research directions where we think the interface between these fields will have the most impact and become potential game changers. In our declared agenda for AI on Earth sciences, we aim to inspire researchers, especially the younger generations, to tackle these challenges for a real advance of remote sensing and the geosciences.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 58 of its 89 references that are in the map. First posted on arXiv 11 Apr 2021.

Builds on

58 of 89 references.

References · 58

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2015VQA: Visual Question Answeringcited by 2,489
  4. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  5. 2018Sanity Checks for Saliency Mapscited by 1,149
  6. 2018VizWiz Grand Challenge: Answering Visual Questions from Blind Peoplecited by 852
  7. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  8. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
  9. 2017A simple neural network module for relational reasoningcited by 345
  10. 2017Temporal Relational Reasoning in Videoscited by 324
  11. 2019Learning De-biased Representations with Biased Representationscited by 199
  12. 2020RSVQA: Visual Question Answering for Remote Sensing Datacited by 197
38 more
  1. 2015Wide-Area Image Geolocalization with Aerial Reference Imagerycited by 185
  2. 2018Multimodal Explanations: Justifying Decisions and Pointing to the Evidencecited by 179
  3. 2016Theory-guided Data Science: A New Paradigm for Scientific Discovery from Datacited by 169
  4. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 165
  5. 2019A Meta-Transfer Objective for Learning to Disentangle Causal Mechanismscited by 158
  6. 2017Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modelingcited by 130
  7. 2017Deep learning in remote sensing: a reviewcited by 128
  8. 2020Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matchingcited by 125
  9. 2018Discovering physical concepts with neural networkscited by 117
  10. 2020Robust Learning Through Cross-Task Consistencycited by 105
  11. 2017Building machines that adapt and compute like brainscited by 99
  12. 2017Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledgecited by 95
  13. 2019Bridging the Domain Gap for Ground-to-Aerial Image Matchingcited by 95
  14. 2019Optimal Feature Transport for Cross-View Image Geo-Localizationcited by 90
  15. 2019MFAS: Multimodal Fusion Architecture Searchcited by 84
  16. 2016Label-Free Supervision of Neural Networks with Physics and Domain Knowledgecited by 74
  17. 2019Deep Learning for Classification of Hyperspectral Data: A Comparative Reviewcited by 51
  18. 2019Interpretability Beyond Classification Output: Semantic Bottleneck Networkscited by 49
  19. 2017Fair Kernel Learningcited by 39
  20. 2018Building Instance Classification Using Street View Imagescited by 33
  21. 2019A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenescited by 33
  22. 2017See, Hear, and Read: Deep Aligned Representationscited by 32
  23. 2020OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensingcited by 30
  24. 2019Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solutioncited by 26
  25. 2019Country-wide high-resolution vegetation height mapping with Sentinel-2cited by 22
  26. 2020Explaining Deep Neural Networks and Beyond: A Review of Methods and Applicationscited by 22
  27. 2018Interpretable Intuitive Physics Modelcited by 19
  28. 2020SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioningcited by 19
  29. 2020Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sourcescited by 19
  30. 2020Learning a Dynamic Map of Visual Appearancecited by 18
  31. 2020ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videoscited by 17
  32. 2017A Unified Model for Near and Remote Sensingcited by 14
  33. 2020Contextual Semantic Interpretabilitycited by 14
  34. 2020Physics-Aware Gaussian Processes in Remote Sensingcited by 14
  35. 2016Understanding and Mapping Natural Beautycited by 10
  36. 2017Towards seamless multi-view scene analysis from satellite to street-levelcited by 10
  37. 2019Semantically Interpretable Activation Maps: what-where-how explanations within CNNscited by 10
  38. 2019Learning Geo-Temporal Image Featurescited by 9

50 most cited of 58 All 58 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 6; 2024: 6; 2025: 7; 2026: 3.

27 papers.

Sort:

By area:

  1. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  2. 2024Mission Critical -- Satellite Data is a Distinct Modality in Machine Learningcited by 76
  3. 2021On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classificationcited by 33
  4. 2024Large Language Models for Captioning and Retrieving Remote Sensing Imagescited by 30
  5. 2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
  6. 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trendscited by 23
  7. 2022Hephaestus: A large scale multitask dataset towards InSAR understandingcited by 10
  8. 2024Knowledge-aware Text-Image Retrieval for Remote Sensing Imagescited by 9
  9. 2025SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Imagescited by 9
  10. 2021How to find a good image-text embedding for remote sensing visual question answering?cited by 6
  11. 2021SyntEO: Synthetic Data Set Generation for Earth Observation and Deep Learning -- Demonstrated for Offshore Wind Farm Detectioncited by 6
  12. 2025Probabilistic Machine Learning for Noisy Labels in Earth Observationcited by 6
15 more
  1. 2023The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluationcited by 4
  2. 2024Responsible AI for Earth Observationcited by 4
  3. 2024Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentationcited by 4
  4. 2025Uncertainty-Aware Deep Learning for Wildfire Danger Forecastingcited by 4
  5. 2025GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidancecited by 3
  6. 2026ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AIcited by 3
  7. 2023Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunitiescited by 2
  8. 2025FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensingcited by 2
  9. 2025Artificial Intelligence for Atmospheric Sciences: A Research Roadmapcited by 2
  10. 2025Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaborationcited by 2
  11. 2023Improving embedding of graphs with missing data by soft manifoldscited by 1
  12. 2024Applications of Explainable artificial intelligence in Earth system sciencecited by 1
  13. 2023Stable Rivers: A Case Study in the Application of Text-to-Image Generative Models for Earth Sciencescited by 0
  14. 2026Toward Artificial Intelligence Enabled Earth System Couplingcited by 0
  15. 2026Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?cited by 0