This repository provides a comprehensive overview of deep learning techniques for satellite and aerial imagery analysis, targeting researchers and practitioners in remote sensing and computer vision. It aims to consolidate and categorize a vast array of methods for tasks like classification, segmentation, object detection, and more, serving as a valuable reference for understanding the current landscape of deep learning applications in this domain.

How It Works

The repository is structured by task, detailing various deep learning architectures, models, and algorithms applied to satellite and aerial imagery. It covers a broad spectrum of techniques, from foundational CNNs and Vision Transformers to more specialized approaches like Graph Neural Networks and attention mechanisms, often linking to specific GitHub repositories and research papers for implementation details and further exploration.

Quick Start & Requirements

This repository is a curated list of techniques and does not have a direct installation or execution command. Users are expected to navigate the linked resources for specific implementations, which will have their own requirements (e.g., Python, PyTorch, TensorFlow, specific hardware like GPUs).

Highlighted Details

• Extensive categorization of deep learning techniques across numerous remote sensing tasks.
• Links to a vast number of GitHub repositories and research papers for practical implementation.
• Covers a wide range of data modalities and sensor types (optical, SAR, LiDAR).
• Includes emerging areas like large vision-language models and foundation models for Earth Observation.

Maintenance & Community

The repository appears to be a community-driven effort, with numerous contributors implied by the breadth of linked projects. Specific maintenance details or community links (like Discord/Slack) are not provided in the README.

Licensing & Compatibility

The repository itself is a collection of links and does not specify a license. The licensing of individual linked projects will vary and must be checked on their respective repositories.

Limitations & Caveats

As a curated list, the repository's effectiveness relies on the quality and maintenance of the linked external resources. The sheer volume of information can be overwhelming, and some links may become outdated.