Very high resolution (VHR) mapping through remote sensing (RS) imagery presents a new opportunity to inform decision-making and sustainable practices in countless domains. Efficient processing of big VHR data requires automated tools applicable to numerous geographic regions and features. Contemporary RS studies address this challenge by employing deep learning (DL) models for specific datasets or features, which limits their applicability across contexts. The present research aims to overcome this limitation by introducing EcoMapper, a scalable solution to segment arbitrary features in VHR RS imagery. EcoMapper fully automates processing of geospatial data, DL model training, and inference. Models trained with EcoMapper successfully segmented two distinct features in a real-world UAV dataset, achieving scores competitive with prior studies which employed context-specific models. To evaluate EcoMapper, many additional models were trained on permutations of principal field survey characteristics (FSCs). A relationship was discovered allowing derivation of optimal ground sampling distance from feature size, termed Cording Index (CI). A comprehensive methodology for field surveys was developed to ensure DL methods can be applied effectively to collected data. The EcoMapper code accompanying this work is available at https://github.com/hcording/ecomapper .

翻译：通过遥感影像进行超高分辨率制图为无数领域的决策制定和可持续实践提供了新的机遇。高效处理大规模超高分辨率数据需要适用于众多地理区域和特征的自动化工具。当前遥感研究通过采用针对特定数据集或特征的深度学习模型来应对这一挑战，这限制了其跨情境的适用性。本研究旨在通过引入EcoMapper——一种可扩展的解决方案来分割超高分辨率遥感图像中的任意特征，从而克服这一局限。EcoMapper实现了地理空间数据处理、深度学习模型训练与推理的全自动化。使用EcoMapper训练的模型成功在真实无人机数据集中分割了两个不同特征，其得分与先前采用情境特定模型的研究具有竞争力。为评估EcoMapper，我们基于主要野外调查特征的不同排列组合训练了多个附加模型。研究发现了一种关系，允许从特征尺寸推导出最佳地面采样距离，称为科丁指数。本研究还开发了一套完整的野外调查方法，以确保深度学习技术能够有效应用于采集数据。本工作附带的EcoMapper代码可在https://github.com/hcording/ecomapper获取。