This work leverages neural radiance fields and remote sensing for forestry applications. Here, we show neural radiance fields offer a wide range of possibilities to improve upon existing remote sensing methods in forest monitoring. We present experiments that demonstrate their potential to: (1) express fine features of forest 3D structure, (2) fuse available remote sensing modalities and (3), improve upon 3D structure derived forest metrics. Altogether, these properties make neural fields an attractive computational tool with great potential to further advance the scalability and accuracy of forest monitoring programs.

翻译：本工作将神经辐射场与遥感技术应用于林业领域。研究表明，神经辐射场在森林监测中为改进现有遥感方法提供了广泛可能性。我们通过实验论证了其三大潜力：(1) 表达森林三维结构的精细特征，(2) 融合多模式遥感数据，(3) 改进基于三维结构的森林度量指标。综合而言，这些特性使神经场成为极具吸引力的计算工具，有望进一步推动森林监测项目的可扩展性与精确性。