Electro-Optical and Infrared Aerial Dataset covering Road Networks & Environment in Nome,Alaska-Site1, Sept. 2022.

Remote sensing enables the rapid, accurate, and non-destructive collection of geospatial data, facilitating access to remote and environmentally sensitive regions in near real-time. This dataset comprises electro-optical (EO) and infrared (IR) aerial imagery of road networks and surrounding infrastructure in remote areas outside Nome, Alaska. The data was collected during an autumn 2022 survey, coinciding with the aftermath of Typhoon Merbok. Some of the captured imagery documents storm-induced damage before initial repairs were carried out, providing critical information for infrastructure assessment and disaster impact analysis. This high-resolution dataset contributes to ongoing research on permafrost thaw-related subsidence, cold-region infrastructure resilience, and geospatial analysis in Arctic environments. Additionally, it serves as a valuable reference for object classification and remote sensing applications in permafrost-affected landscapes. The dataset supports interdisciplinary studies in cold-region geoscience, environmental monitoring, and predictive modeling of climate-induced landscape changes.遥感技术可实现地理空间数据的快速、精准且无损采集，能够近乎实时地获取偏远且生态敏感区域的相关数据。本数据集涵盖美国阿拉斯加州诺姆市外围偏远区域的道路网络及周边基础设施的光电（EO, electro-optical）与红外（IR, infrared）航空影像。该数据采集于2022年秋季的一次航空勘测任务，恰逢台风梅尔博克（Typhoon Merbok）过境后的灾后阶段。部分采集的影像记录了初期修复作业开展前风暴造成的损毁情况，可为基础设施评估与灾害影响分析提供关键支撑数据。这套高分辨率数据集可助力北极环境领域的多项前沿研究，涵盖冻土融化引发的地表沉降、寒区基础设施韧性以及北极地理空间分析等方向。此外，该数据集还可为受冻土影响区域的目标分类与遥感应用研究提供宝贵参考依据。本数据集可支撑寒区地球科学、环境监测以及气候变化引发的地表变化预测建模等跨学科研究工作。