# Evaluation Challenges for Geospatial ML

arXiv: https://arxiv.org/abs/2303.18087 · HTML: https://mlsanity.com/paper/2303.18087/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery](https://mlsanity.com/paper/2010.08168/) (2020)
- [Using satellite imagery to understand and promote sustainable development](https://mlsanity.com/paper/2010.06988/) (2020)
- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [Micro-Estimates of Wealth for all Low- and Middle-Income Countries](https://mlsanity.com/paper/2104.07761/) (2021)
- [Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction](https://mlsanity.com/paper/1908.07805/) (2019)
- [Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation](https://mlsanity.com/paper/2005.14263/) (2020)
- [Fast building segmentation from satellite imagery and few local labels](https://mlsanity.com/paper/2206.05377/) (2022)
- [Auxiliary-task learning for geographic data with autoregressive embeddings](https://mlsanity.com/paper/2006.10461/) (2020)
- [Spatial machine-learning model diagnostics: a model-agnostic distance-based approach](https://mlsanity.com/paper/2111.08478/) (2021)
- [Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information](https://mlsanity.com/paper/2003.05955/) (2020)
- [Geostatistical Learning: Challenges and Opportunities](https://mlsanity.com/paper/2102.08791/) (2021)

## Built on by (most cited first, 10 of 10)

- [Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning](https://mlsanity.com/paper/2402.01444/) (2024)
- [Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It](https://mlsanity.com/paper/2312.06420/) (2023)
- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?](https://mlsanity.com/paper/2307.02575/) (2023)
- [Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy](https://mlsanity.com/paper/2305.01783/) (2023)
- [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)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)
- [Large-scale School Mapping using Weakly Supervised Deep Learning for Universal School Connectivity](https://mlsanity.com/paper/2412.14870/) (2024)
- [MIND the Gap: A Geographic Implicit Neural Representation with Adjustable Spatial Scale](https://mlsanity.com/paper/2609.25454/) (2026)
- [FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models](https://mlsanity.com/paper/2610.05790/) (2026)

## Abstract (arXiv)

As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability. Geospatial machine learning has key distinctions from other learning paradigms, and as such, the correct way to measure performance of spatial machine learning outputs has been a topic of debate. In this paper, I delineate unique challenges of model evaluation for geospatial machine learning with global or remotely sensed datasets, culminating in concrete takeaways to improve evaluations of geospatial model performance.