Semantic Neural Radiance Fields for Multi-Date Satellite Data
This work addresses the challenge of 3D semantic reconstruction from satellite data for remote sensing applications, representing an incremental advancement by adapting existing NeRF methods to a specific domain.
The authors tackled the problem of creating 3D semantic representations from multi-date satellite images by proposing a satellite-specific Neural Radiance Fields model that improves noisy input labels and addresses temporal inconsistencies in non-stationary categories like vehicles, achieving enhanced color prediction.
In this work we propose a satellite specific Neural Radiance Fields (NeRF) model capable to obtain a three-dimensional semantic representation (neural semantic field) of the scene. The model derives the output from a set of multi-date satellite images with corresponding pixel-wise semantic labels. We demonstrate the robustness of our approach and its capability to improve noisy input labels. We enhance the color prediction by utilizing the semantic information to address temporal image inconsistencies caused by non-stationary categories such as vehicles. To facilitate further research in this domain, we present a dataset comprising manually generated labels for popular multi-view satellite images. Our code and dataset are available at https://github.com/wagnva/semantic-nerf-for-satellite-data.