# PhilEO Bench: Evaluating Geo-Spatial Foundation Models

arXiv: https://arxiv.org/abs/2401.04464 · HTML: https://mlsanity.com/paper/2401.04464/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [Multi-task Self-Supervised Visual Learning](https://mlsanity.com/paper/1708.07860/) (2017)
- [Geography-Aware Self-Supervised Learning](https://mlsanity.com/paper/2011.09980/) (2020)
- [SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery](https://mlsanity.com/paper/2311.17179/) (2023)
- [Foundation Models for Generalist Geospatial Artificial Intelligence](https://mlsanity.com/paper/2310.18660/) (2023)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [Towards Geospatial Foundation Models via Continual Pretraining](https://mlsanity.com/paper/2302.04476/) (2023)
- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (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)
- [Geographical Knowledge-driven Representation Learning for Remote Sensing Images](https://mlsanity.com/paper/2107.05276/) (2021)
- [USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery](https://mlsanity.com/paper/2312.02199/) (2023)
- [The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning](https://mlsanity.com/paper/2303.00106/) (2023)
- [Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters](https://mlsanity.com/paper/2305.13456/) (2023)
- [Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification](https://mlsanity.com/paper/2203.06041/) (2022)

## Built on by (most cited first, 25 of 25)

- [TerraMind: Large-Scale Generative Multimodality for Earth Observation](https://mlsanity.com/paper/2504.11171/) (2025)
- [Towards a Unified Copernicus Foundation Model for Earth Vision](https://mlsanity.com/paper/2503.11849/) (2025)
- [On the Foundations of Earth and Climate Foundation Models](https://mlsanity.com/paper/2405.04285/) (2024)
- [No One Knows the State of the Art in Geospatial Foundation Models](https://mlsanity.com/paper/2605.12678/) (2026)
- [Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI](https://mlsanity.com/paper/2406.18295/) (2024)
- [MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training](https://mlsanity.com/paper/2602.06285/) (2026)
- [SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications](https://mlsanity.com/paper/2507.03578/) (2025)
- [Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal](https://mlsanity.com/paper/2508.00858/) (2025)
- [Ice-FMBench: A Foundation Model Benchmark for Sea Ice Type Segmentation](https://mlsanity.com/paper/2503.22516/) (2025)
- [SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology](https://mlsanity.com/paper/2504.18256/) (2025)
- [TESSERA v2: Scaling Pixel-wise Earth Foundation Models](https://mlsanity.com/paper/2607.03949/) (2026)
- [Foundation Models for Environmental Science: A Survey of Emerging Frontiers](https://mlsanity.com/paper/2504.04280/) (2025)
- [Towards Efficient Benchmarking of Foundation Models in Remote Sensing: A Capabilities Encoding Approach](https://mlsanity.com/paper/2505.03299/) (2025)
- [Benchmarking Geospatial Foundation Models for Agriculture Applications](https://mlsanity.com/paper/2606.29664/) (2026)
- [SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery](https://mlsanity.com/paper/2405.02512/) (2024)
- [Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge](https://mlsanity.com/paper/2502.13818/) (2025)
- [Scaling Laws for Geospatial Foundation Models: A case study on PhilEO Bench](https://mlsanity.com/paper/2506.14765/) (2025)
- [Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data](https://mlsanity.com/paper/2604.07092/) (2026)
- [Improving satellite imagery segmentation using multiple Sentinel-2 revisits](https://mlsanity.com/paper/2409.17363/) (2024)
- [CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models](https://mlsanity.com/paper/2502.13734/) (2025)
- [Improving EO Foundation Models with Confidence Assessment for enhanced Semantic segmentation](https://mlsanity.com/paper/2406.18279/) (2024)
- [Rethinking deep learning: linear regression remains a key benchmark in predicting terrestrial water storage](https://mlsanity.com/paper/2510.10799/) (2025)
- [Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modelling](https://mlsanity.com/paper/2511.11706/) (2025)
- [Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference](https://mlsanity.com/paper/2608.15282/) (2026)
- [RSPDBench: Benchmarking Vision Foundation Models on Earth Observation Tasks Under Physically Grounded Remote-Sensing Product Degradations](https://mlsanity.com/paper/2609.23427/) (2026)

## Abstract (arXiv)

Massive amounts of unlabelled data are captured by Earth Observation (EO) satellites, with the Sentinel-2 constellation generating 1.6 TB of data daily. This makes Remote Sensing a data-rich domain well suited to Machine Learning (ML) solutions. However, a bottleneck in applying ML models to EO is the lack of annotated data as annotation is a labour-intensive and costly process. As a result, research in this domain has focused on Self-Supervised Learning and Foundation Model approaches. This paper addresses the need to evaluate different Foundation Models on a fair and uniform benchmark by introducing the PhilEO Bench, a novel evaluation framework for EO Foundation Models. The framework comprises of a testbed and a novel 400 GB Sentinel-2 dataset containing labels for three downstream tasks, building density estimation, road segmentation, and land cover classification. We present experiments using our framework evaluating different Foundation Models, including Prithvi and SatMAE, at multiple n-shots and convergence rates.