# Better, Not Just More: Data-Centric Machine Learning for Earth Observation

arXiv: https://arxiv.org/abs/2312.05327 · HTML: https://mlsanity.com/paper/2312.05327/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 142 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 30 of 48)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [Domino: Discovering Systematic Errors with Cross-Modal Embeddings](https://mlsanity.com/paper/2203.14960/) (2022)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018)
- [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](https://mlsanity.com/paper/1906.07789/) (2019)
- [On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2304.06798/) (2023)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [A Distributional Framework for Data Valuation](https://mlsanity.com/paper/2002.12334/) (2020)
- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data](https://mlsanity.com/paper/2205.03147/) (2022)
- [HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks](https://mlsanity.com/paper/2102.02515/) (2021)
- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [Predicting into unknown space? Estimating the area of applicability of spatial prediction models](https://mlsanity.com/paper/2005.07939/) (2020)
- [Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study](https://mlsanity.com/paper/2010.07830/) (2020)
- [A survey of active learning algorithms for supervised remote sensing image classification](https://mlsanity.com/paper/2104.07784/) (2021)
- [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022)

## Built on by (most cited first, 19 of 19)

- [Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning](https://mlsanity.com/paper/2402.01444/) (2024)
- [AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities](https://mlsanity.com/paper/2412.14123/) (2024)
- [TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation](https://mlsanity.com/paper/2506.06281/) (2025)
- [The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation](https://mlsanity.com/paper/2503.15683/) (2025)
- [Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas](https://mlsanity.com/paper/2411.14354/) (2024)
- [FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping](https://mlsanity.com/paper/2506.07080/) (2025)
- [How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?](https://mlsanity.com/paper/2501.12535/) (2025)
- [SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology](https://mlsanity.com/paper/2504.18256/) (2025)
- [Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery](https://mlsanity.com/paper/2507.13385/) (2025)
- [Responsible AI for Earth Observation](https://mlsanity.com/paper/2405.20868/) (2024)
- [Core-Set Selection for Data-efficient Land Cover Segmentation](https://mlsanity.com/paper/2505.01225/) (2025)
- [CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts](https://mlsanity.com/paper/2509.14104/) (2025)
- [Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions](https://mlsanity.com/paper/2510.16207/) (2025)
- [Operational Change Detection for Geographical Information: Overview and Challenges](https://mlsanity.com/paper/2503.14109/) (2025)
- [On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?](https://mlsanity.com/paper/2503.19719/) (2025)
- [T3S: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series](https://mlsanity.com/paper/2506.12885/) (2025)
- [AI-Derived Structural Building Intelligence for Urban Resilience: An Application in Saint Vincent and the Grenadines](https://mlsanity.com/paper/2509.18182/) (2025)
- [Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation](https://mlsanity.com/paper/2603.00604/) (2026)
- [Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings](https://mlsanity.com/paper/2609.15773/) (2026)

## Abstract (arXiv)

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchmark datasets that lack strong real-world relevance. Furthermore, the performance of many methods has already saturated on these datasets. We argue that a shift from a model-centric view to a complementary data-centric perspective is necessary for further improvements in accuracy, generalization ability, and real impact on end-user applications. Furthermore, considering the entire machine learning cycle-from problem definition to model deployment with feedback-is crucial for enhancing machine learning models that can be reliable in unforeseen situations. This work presents a definition as well as a precise categorization and overview of automated data-centric learning approaches for geospatial data. It highlights the complementary role of data-centric learning with respect to model-centric in the larger machine learning deployment cycle. We review papers across the entire geospatial field and categorize them into different groups. A set of representative experiments shows concrete implementation examples. These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.