Related papers: Semantic Neural Radiance Fields for Multi-Date Satellite Data

Semantic Neural Radiance Fields for Multi-Date Satellite Data

URL: http://arxiv.org/abs/2502.16992v1
Date: Mon, 24 Feb 2025 09:26:48 GMT
Title: Semantic Neural Radiance Fields for Multi-Date Satellite Data
Authors: Valentin Wagner, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens,
Abstract summary: We propose a satellite specific Neural Radiance Fields (NeRF) model capable to obtain a three-dimensional semantic representation of the scene.<n>The model derives the output from a set of multi-date satellite images with corresponding pixel-wise semantic labels.<n>We enhance the color prediction by utilizing the semantic information to address temporal image inconsistencies.
Score: 4.174845397893041
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: 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.

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