The content presents a method for segmenting crop field boundaries in satellite images using multi-region transfer learning. The key highlights are:
Field boundary delineation is a crucial task for various agricultural applications, but it presents unique challenges compared to traditional computer vision datasets, such as the importance of the temporal dimension, the availability of multi-spectral data, and the limited labeled data in many regions.
The authors propose a multi-region transfer learning approach to address the limited labeled data problem. They pre-train a model on a large labeled dataset from one region, fine-tune it using a smaller labeled dataset from another region, and then evaluate the performance on a target region with limited or no labeled data.
The authors use the Spatio-Temporal U-Net (ST-U-Net) architecture as the base model and evaluate its performance on datasets from France, South Africa, and Kenya. They show that the multi-region transfer learning approach significantly boosts performance compared to training on the target region alone or without fine-tuning.
The authors also analyze the performance of different model backbones (ResNet-18, ResNet-50, ResNet-101) and the impact of using multi-temporal satellite imagery versus single-time imagery.
The results demonstrate the effectiveness of the multi-region transfer learning approach, particularly for the Kenya dataset, which has very limited labeled data. The authors also discuss the challenges of small field sizes in Kenya and the benefits of using commercial high-resolution satellite data in conjunction with freely available lower-resolution data.
The authors make their implementation and datasets publicly available to enable use by end-users and serve as a benchmark for future work on this task.
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