# Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward

arXiv: https://arxiv.org/abs/2305.08413 · HTML: https://mlsanity.com/paper/2305.08413/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 177 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 30 of 60)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [MultiMAE: Multi-modal Multi-task Masked Autoencoders](https://mlsanity.com/paper/2204.01678/) (2022)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [Reinforced Continual Learning](https://mlsanity.com/paper/1805.12369/) (2018)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (2023)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)

## Built on by (most cited first, 28 of 28)

- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis](https://mlsanity.com/paper/2407.03653/) (2024)
- [Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model](https://mlsanity.com/paper/2501.00895/) (2025)
- [RemoteSAM: Towards Segment Anything for Earth Observation](https://mlsanity.com/paper/2505.18022/) (2025)
- [Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems](https://mlsanity.com/paper/2408.07828/) (2024)
- [IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI](https://mlsanity.com/paper/2410.04135/) (2024)
- [Self-Supervised Cross-Modal Text-Image Time Series Retrieval in Remote Sensing](https://mlsanity.com/paper/2501.19043/) (2025)
- [Localized, High-resolution Geographic Representations with Slepian Functions](https://mlsanity.com/paper/2602.00392/) (2026)
- [Tile and Slide : A New Framework for Scaling NeRF from Local to Global 3D Earth Observation](https://mlsanity.com/paper/2507.01631/) (2025)
- [Process-Guided Concept Bottleneck Model](https://mlsanity.com/paper/2601.10562/) (2026)
- [SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling](https://mlsanity.com/paper/2504.13310/) (2025)
- [Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing](https://mlsanity.com/paper/2506.21312/) (2025)
- [InstaGeo: Compute-Efficient Geospatial Machine Learning from Data to Deployment](https://mlsanity.com/paper/2510.05617/) (2025)
- [EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence](https://mlsanity.com/paper/2511.10834/) (2025)
- [BotaCLIP: Contrastive Learning for Botany-Aware Representation of Earth Observation Data](https://mlsanity.com/paper/2511.21194/) (2025)
- [Leveraging Land Cover Priors for Isoprene Emission Super-Resolution](https://mlsanity.com/paper/2503.18658/) (2025)
- [Lightweight Cloud Masking Models for On-Board Inference in Hyperspectral Imaging](https://mlsanity.com/paper/2507.08052/) (2025)
- [An Efficient and Effective Encoder Model for Vision and Language Tasks in the Remote Sensing Domain](https://mlsanity.com/paper/2512.15531/) (2025)
- [The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?](https://mlsanity.com/paper/2603.20305/) (2026)
- [Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI](https://mlsanity.com/paper/2604.24492/) (2026)
- [Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing](https://mlsanity.com/paper/2605.05590/) (2026)
- [Clustering Guided Domain-Specific Pretrained Foundation Model for Very High-Resolution Arctic Remote Sensing](https://mlsanity.com/paper/2605.30467/) (2026)
- [Event-Based Vision in Space: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2606.01280/) (2026)
- [BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection](https://mlsanity.com/paper/2606.05586/) (2026)
- [On the Disagreement in Perturbation-based xAI -- Benchmarking Perturbation Choices for Flood Detection from SAR Images](https://mlsanity.com/paper/2607.14743/) (2026)
- [Hybrid Quantum CNN for Cross-Sensor Spaceborne Volcanic Thermal Activity Recognition Worldwide](https://mlsanity.com/paper/2608.00069/) (2026)
- [Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery](https://mlsanity.com/paper/2608.00072/) (2026)
- [FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification](https://mlsanity.com/paper/2610.00693/) (2026)

## Abstract (arXiv)

Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based information. The promises, as well as the current challenges of these developments, are highlighted under dedicated sections. Specifically, we cover the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and (viii) the much-needed discussion of ethical and societal issues related to the massive use of ML technologies in EO.