Research Article
Spatial Quantification of the Urban Self-Healing Index Using IoT-Based Noise GeoTIFF Data
@INPROCEEDINGS{10.4108/eai.22-5-2026.2365244, author={Jiaxin Liu}, title={Spatial Quantification of the Urban Self-Healing Index Using IoT-Based Noise GeoTIFF Data}, proceedings={Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore}, publisher={EAI}, proceedings_a={ICIAAI}, year={2026}, month={8}, keywords={Urban self-healing index (U-SHI) IoT noise sensors spatial analysis GeoTIFF data urban resilience smart cities}, doi={10.4108/eai.22-5-2026.2365244} }- Jiaxin Liu
Year: 2026
Spatial Quantification of the Urban Self-Healing Index Using IoT-Based Noise GeoTIFF Data
ICIAAI
EAI
DOI: 10.4108/eai.22-5-2026.2365244
Abstract
Internet of Things (IoT) sensor networks aim to ensure the stability of this growth. Nevertheless, the spatial recovery capacity of cities in the face of environmental disturbances has not received much coverage despite the majority of available studies using noise monitoring, prediction, or anomaly detection as their objectives. This paper recommends a spatial measure, the Urban Self-Healing Index (U-SHI), to measure the performance of cities with respect to recovery in the face of noise disturbances. K-means Studio is applicable to locate various noise areas and to separate disturbance hotspots and comparatively quiet regions. Three spatial indicators are obtained based on the clustering results, which are the noise recovery gradient, the ratio of quiet areas, and the compactness of noise hotspots. Normalization takes place on these indicators, and an index, the U-SHI, is built using them. The conclusions show that the suggested framework reflects the spatial variations in recovery capacities.
