The February 6, 2023 earthquakes caused severe structural damage in the Kahramanmaraş, Hatay, and Gaziantep regions, highlighting the critical need for comprehensive geospatial analyses in post-disaster response efforts. This study presents a 3D point cloud dataset derived from UAV imagery to support damage assessment and urban reconstruction efforts. A total of 268 UAV images covering a small region of Hatay that is approximately of 31,000 m2 were automatically collected using a DJI Mavic 2 Pro. This automated approach ensured the systematic acquisition of high-resolution aerial data. The dataset spans on one of the most utilized urban roads and its surroundings. The collected 2D images were processed in Pix4D software that reconstructs images using photogrammetry techniques including feature matching, structure-from-motion, dense point cloud generation, and georeferencing to produce accurate 2D maps and 3D models. The resulting dataset provides critical spatial information for quantitative damage classification, integration with Geographic Information Systems (GIS), infrastructure planning, and long-term monitoring of the reconstruction process. Furthermore, challenges encountered in UAV-based 3D modeling—such as data gaps, image overlap issues, and processing limitations—are also addressed. This study demonstrates the effectiveness and potential applications of automated UAV-based 3D mapping in post-disaster recovery and emergency response.