GEO-Bench: Toward Foundation Models for Earth Monitoring (2306.03831v2)

Abstract: Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

• The paper introduces a novel benchmark combining six classification and six segmentation tasks tailored for Earth monitoring.
• It demonstrates rigorous evaluation by testing 20 baseline models, comparing convolutional and transformer architectures on remote sensing data.
• It outlines data augmentation techniques and statistical methods like IQM with bootstrapping to ensure reliable performance assessments.

Introduction to GEO-Bench

In the rapidly evolving landscape of machine learning, the concept of foundation models has emerged prominently. These models are designed with robust pre-training on massive, diverse datasets, thereby enabling strong performance on a broad spectrum of downstream tasks. Although the benefits of foundation models have been well-established in domains like natural language processing and image recognition, their potential in Earth monitoring remains relatively untapped. With the climate challenges facing our planet, there is a pressing need for sophisticated tools to observe and understand our environment more comprehensively.

The Need for Specialized Earth Monitoring Models

Traditional image datasets from the usual machine learning contexts differ significantly from Earth observation data. The perspective, scale, temporal properties, and spectrum of data gathered from satellite imagery entail unique considerations. Earth monitoring tasks deal with large-scale imagery captured from orbit, spanning multiple spectral bands, often with temporal dimensions due to varying satellite revisit times. These factors necessitate models that can effectively interpret such complex data and derive useful insights for applications like methane source detection, forest carbon quantification, and crop monitoring, among others.

Benchmarking for Progress

To ignite innovation in this area, the GEO-Bench proposes a benchmark consisting of six classification and six segmentation tasks selected to represent the nuanced challenges of Earth observation. This benchmark is not just a mere collection of datasets; it is carefully crafted to foster methodical progress in Earth monitoring. The tasks cover a spectrum of sensors, modalities, and spatial resolutions, providing a platform to test and refine foundation models tailored for Earth monitoring.

Evaluating Models with GEO-Bench

GEO-Bench promotes rigorous model evaluation and facilitates scientific advancement by providing a structured approach to assess model quality. For instance, models can be fine-tuned for specificity after pre-training on unsupervised data, and GEO-Bench recommends using particular data augmentation techniques for Earth monitoring contexts. Moreover, the benchmark encourages reporting results using methods like Interquartile Mean (IQM) with bootstrapping to ensure statistical reliability and comparability.

In their comprehensive experimentation, the authors of GEO-Bench evaluated 20 baseline models across the benchmark tasks. These baselines range from simple models to sophisticated ones such as convolutional and transformer neural network architectures. Key findings pointed to the particular efficacy of newer architectures in managing Earth monitoring tasks, underlining the significance of designing and improving foundation models specifically catered to the peculiarities of remote sensing data.

By establishing GEO-Bench, researchers striving to refine machine learning techniques for Earth monitoring now have a valuable compass guiding their efforts. The benchmark is expected to stimulate innovative models that can generalize across various Earth monitoring tasks, potentially unlocking new applications and contributing significantly to our understanding and management of environmental challenges.