# Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation

arXiv: https://arxiv.org/abs/2412.13394 · HTML: https://mlsanity.com/paper/2412.13394/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 24 of 24)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [Revisiting the Calibration of Modern Neural Networks](https://mlsanity.com/paper/2106.07998/) (2021)
- [Extremely Simple Activation Shaping for Out-of-Distribution Detection](https://mlsanity.com/paper/2209.09858/) (2022)
- [DICE: Leveraging Sparsification for Out-of-Distribution Detection](https://mlsanity.com/paper/2111.09805/) (2021)
- [Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning](https://mlsanity.com/paper/2402.01444/) (2024)
- [In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness](https://mlsanity.com/paper/2012.04550/) (2020)
- [Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation](https://mlsanity.com/paper/2409.16252/) (2024)
- [Detection of out-of-distribution samples using binary neuron activation patterns](https://mlsanity.com/paper/2212.14268/) (2022)
- [NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation](https://mlsanity.com/paper/2311.11961/) (2023)
- [Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models](https://mlsanity.com/paper/2404.12667/) (2024)
- [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)
- [Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels](https://mlsanity.com/paper/2404.00179/) (2024)
- [Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space](https://mlsanity.com/paper/2203.08549/) (2022)
- [Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression](https://mlsanity.com/paper/2407.13141/) (2024)

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

- [From Pixels to Patches: Pooling Strategies for Earth Embeddings](https://mlsanity.com/paper/2603.02080/) (2026)
- [SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation](https://mlsanity.com/paper/2511.10370/) (2025)
- [Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge](https://mlsanity.com/paper/2502.13818/) (2025)
- [Latent Domain Modeling Improves Robustness to Geographic Shifts](https://mlsanity.com/paper/2503.02036/) (2025)
- [Uncertainty evaluation of segmentation models for Earth observation](https://mlsanity.com/paper/2510.19586/) (2025)
- [Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutions](https://mlsanity.com/paper/2512.04034/) (2025)
- [YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction](https://mlsanity.com/paper/2604.00940/) (2026)
- [OT on the Map: Quantifying Domain Shifts in Geographic Space](https://mlsanity.com/paper/2604.16220/) (2026)
- [Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts](https://mlsanity.com/paper/2608.16614/) (2026)
- [MVT: Mask-Grounded Vision-Language Models for Taxonomy-Aligned Land-Cover Tagging](https://mlsanity.com/paper/2509.18693/) (2025)

## Abstract (arXiv)

Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from in-distribution (ID) data. However, existing methods either assume access to OOD data or compromise primary task performance, limiting real-world use. We introduce TARDIS, a post-hoc OOD detection method designed for scalable geospatial deployment. Our core innovation lies in generating surrogate distribution labels by leveraging ID data within the feature space. TARDIS takes a pre-trained model, ID data, and data from an unknown distribution (WILD), separates WILD into surrogate ID and OOD labels based on internal activations, and trains a binary classifier to detect distribution shifts. We validate on EuroSAT and xBD across 17 setups covering covariate and semantic shifts, showing near-upper-bound surrogate labeling performance in 13 cases and matching the performance of top post-hoc activation- and scoring-based methods. Finally, deploying TARDIS on Fields of the World reveals actionable insights into pre-trained model behavior at scale. The code is available at hrefhttps://github.com/microsoft/geospatial-ood-detectionhttps://github.com/microsoft/geospatial-ood-detection