rico-hdl: A Fast and Easy-To-Use Remote Sensing Image Format Converter for Deep-Learning

rico-hdl is a fast and easy-to-use remote sensing image format converter for high-throughput deep-learning (rico-hdl). The tool converts the remote sensing images into a DL-optimized format. The resulting output will provide significantly higher throughput than the original remote sensing images (patches) and should be used instead of the unprocessed dataset. The data is encoded in a DL-framework independent format, ensuring flexible use.

BigEarthNet v2.0 Pretrained Model Weights

We provide weights for several different pretrained models. The model weights for the best-performing model, based on the macro average precision score on the recommended test split, have been uploaded. All models have been trained using: i) BigEarthNet-S1 data only (S1), ii) BigEarthNet-S2 data only (S2), or iii) both BigEarthNet-S1 and -S2 (S1+S2) together.

ConfigILM: A Configurable Image-Language Model Library

The ConfigILM library is a state-of-the-art tool for Python developers seeking to rapidly and iteratively develop image and language models within the pytorch framework. This open-source library provides a convenient implementation for seamlessly combining models from two of the most popular pytorch libraries, the highly regarded timm and huggingface. With an extensive collection of nearly 1000 image and over 100 language models, with an additional 120,000 community-uploaded models in the huggingface model collection, ConfigILM offers a diverse range of model combinations that require minimal implementation effort. Its vast array of models makes it an unparalleled resource for developers seeking to create innovative and sophisticated image-language models with ease.

parallelCollGS: Parallel Download from Sentinel Collaborative Ground Segments

This repository provides a python toolchain for parallel queries to download Sentinel 1, 2, and 3 products from a varying number of collaborative ground segments. This toolchain abstracts sentinelsat Python API client to support parallelized mirror access, and thus provides simultaneous access to both high-speed and high-coverage mirrors while reusing the workflow of the non-parallelized client. While keeping as much of the original client’s workflow intact as possible, a fault-tolerant mechanism is included in parallelCollGS for accessing multiple mirrors in parallel. In addition, parallelCollGS uses a scheduling strategy for concurrent downloads to ensure optimal utilization of the available bandwidth. The toolchain provides convenient access to Hadoop Distributed File System (HDFS) via the Apache Hadoop stack based interface for the convenient upload of obtained products.