mlsanityData to 7 Oct 2026

Evaluation Challenges for Geospatial ML

arXiv 2303.18087 ↗First posted 31 Mar 2023cs.LG stat.MLcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 31 Mar 2023.

Builds on

13 of 43 references.

References · 13

  1. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2020A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagerycited by 87
  4. 2020Using satellite imagery to understand and promote sustainable developmentcited by 71
  5. 2021Recent Advances in Domain Adaptation for the Classification of Remote Sensing Datacited by 70
  6. 2021Micro-Estimates of Wealth for all Low- and Middle-Income Countriescited by 25
  7. 2019Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial predictioncited by 24
  8. 2020Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validationcited by 14
  9. 2022Fast building segmentation from satellite imagery and few local labelscited by 9
  10. 2020Auxiliary-task learning for geographic data with autoregressive embeddingscited by 8
  11. 2021Spatial machine-learning model diagnostics: a model-agnostic distance-based approachcited by 2
  12. 2020Post-Estimation Smoothing: A Simple Baseline for Learning with Side Informationcited by 1
1 more
  1. 2021Geostatistical Learning: Challenges and Opportunitiescited by 1

Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 3.

8 papers.

  1. 2024Mission Critical -- Satellite Data is a Distinct Modality in Machine Learningcited by 76
  2. 2023Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix Itcited by 44
  3. 2023Better, Not Just More: Data-Centric Machine Learning for Earth Observationcited by 19
  4. 2023How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?cited by 11
  5. 2023Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policycited by 7
  6. 2024Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannascited by 6
  7. 2023Reflections from the Workshop on AI-Assisted Decision Making for Conservationcited by 2
  8. 2024Large-scale School Mapping using Weakly Supervised Deep Learning for Universal School Connectivitycited by 2