Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Abstract: Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data, including the temporal dimension (which is critical for many applications, such as monitoring crop growth) and availability of data from many complementary sensors (which can significantly improve a model's predictive performance). We present Presto (the Pretrained Remote Sensing Transformer), a model pre-trained on remote sensing pixel-timeseries data. By designing Presto specifically for remote sensing data, we can create a significantly smaller but performant model. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.

Overview of "Lightweight, Pre-trained Transformers for Remote Sensing Timeseries"

The paper titled "Lightweight, Pre-trained Transformers for Remote Sensing Timeseries" presents the development and evaluation of Presto (Pretrained Remote Sensing Transformer), a model designed to efficiently handle remote sensing data characterized by its temporal and multimodal composition. Presto is a transformer-based model pre-trained using a self-supervised masked autoencoding approach, particularly fit for remote sensing tasks that involve pixel-timeseries data. This model addresses key challenges such as the scarcity of labeled satellite data and the computational demands typically associated with model deployment, aiming to provide a high-performance, energy-efficient solution.

Motivation and Context

Machine learning applications in remote sensing have the potential to deliver significant societal benefits, including disaster management, environmental monitoring, and resource tracking aligned with sustainable development goals. Despite this potential, the effectiveness of such models is often hampered by the limited availability of labeled data, particularly in under-resourced areas. Furthermore, current self-supervised learning models for remote sensing tend to be computationally expensive and overlook the temporal dimension, which is crucial for applications such as crop monitoring and landcover classification.

Presto's Methodological Framework

Presto is specifically tailored to harness the temporal patterns inherent in remote sensing data and effectively absorb information from numerous complementary sensors. Notably, it employs a lightweight transformer architecture, which vastly reduces the number of trainable parameters and floating-point operations necessary for inference compared to larger models like Vision Transformers (ViTs) or ResNets. This reduction in computational requirements makes Presto more suitable for deployment in resource-constrained environments.

The model utilizes a masked autoencoding strategy during pre-training to promote robust feature extraction across diverse remote sensing modalities. This involves reconstructing masked segments of the input data, fostering resilience to missing data typical in remote sensing datasets due to factors like cloud cover.

Empirical Evaluation

Presto demonstrates outstanding performance across various globally distributed tasks, such as land cover classification and crop type mapping, often outperforming larger models with significantly fewer resources. Through its adaptability, Presto accommodates different downstream tasks by either acting as a transferable feature extractor or being fine-tuned for specific tasks. The Presto model exhibits robust performance even with incomplete inputs, which is crucial given the partial observation nature of many remote sensing datasets.

Implications and Future Directions

The development of Presto highlights the feasibility of applying self-supervised transformers in remote sensing, enabling efficient and effective analysis of pixel-timeseries data in a way that prioritizes computational efficiency. The implications of this work suggest a broader adoption of lightweight models for geospatial tasks, potentially expanding accessibility to machine learning technology for numerous practical applications in environmental monitoring.

For future research, there is a promising avenue for exploring enhanced spatio-temporal aggregation techniques to further bolster Presto's efficacy in scene classification tasks. Also, scaling Presto to handle higher spatial resolutions and incorporating additional data sources could extend its utility. Continued innovation in this direction could further diminish barriers to deploying machine learning solutions for critical societal challenges in remote sensing contexts.