Analyzing urban morphology changes using neural networks
Abstract
Introduction to the problem. Urbanization demands advanced tools to analyze morphological changes caused by hostilities or disasters. This study bridges this gap by integrating artificial neural networks (ANNs) with LiDAR and GIS technologies, focusing on a site in Kharkiv, Ukraine, which was marginally impacted by the 2022 Russian invasion. Our key objective is to quantify urban resilience and transformation under extreme stress.
Review of previous works. Advances in CNNs and RNNs have enabled spatial-temporal analysis of LiDAR and multisource data. Recent methodologies improved feature extraction for urban change detection. However, gaps persisted in hostilities’ zone analysis, airborne and terrestrial LiDAR integration, and interpretability of ANN-driven insights.
Exposition of the main research material. Basics of ANNs for urban studies. This study employs two custom architectures:
1. ANN Similarity (Enhanced): A feedforward network using Mean Squared Error (MSE) loss and cosine similarity to predict dataset similarities. 2. Latest ANN Method: A deeper network with contrastive loss and Euclidean distance, emphasizing dissimilarity detection via convolutional/recurrent layers.
Applications in urban studies. The ANNs in this study were applied to the following from several listed industrial domains:
1. Routine Urban Monitoring: Detecting new constructions/demolitions in Tallinn, ESTONIA. 2. Hostilities Impact Analysis: Identifying war-induced structural changes in Kharkiv, UKRAINE. 3. 3D Feature Extraction: Automating building volumetry and change detection mapping from LiDAR point clouds.
Urban Remote Sensing with LiDAR. LiDAR’s millimeter-level accuracy enabled 3D modeling of urban features (e.g., building footprints, microtopography). Airborne (ALS) and mobile (MLS) LiDAR datasets were processed via proprietary iQ City Change Management (CCM) software, addressing challenges like ALS/MLS alignment and artifact filtering via point-density thresholds.
Case Study: urban change detection using LiDAR to assess hostilities’ impact. Methodology: the study analyzed multitemporal LiDAR datasets: Kharkiv (2019–2022): a 4 km² zone in Northern Saltivka, devastated by shelling. Tallinn (2017–2022): control datasets for routine redevelopment.
CCM Workflow: 1. Building Extraction (BE): identified structural features (Area, Volume, Height). 2. Change Detection (CD): classified changes as Added (new construction), Removed (demolition), or Unchanged.
ANN Analysis for comparing detected changes through Wolfram Mathematica: compared ANSE (similarity-focused) and LANN (dissimilarity-driven) methods. Results: The following changes detected. Kharkiv: 215 Added (pre-war redevelopment) and 51 Removed (war-induced demolitions) changes. The LANN method revealed stark contrasts (score: 0.35 and 0.32-0.42) between war-driven vs. routine redevelopment demolitions, capturing irregular demolitions. Tallinn: predictable redevelopment patterns (scores: 0.60-0.66 and 0.74), validating ANN accuracy for routine changes. Implications: LANN’s sensitivity to hidden features (e.g., structural degradation) gives policymakers detailed guidance for post-war recovery, and its divergence from statistical models highlights AI’s power to reveal unseen urban dynamics.
Conclusion. This research demonstrates how ANNs, fused with LiDAR/GIS, transcend traditional urban monitoring limitations. The framework offers scalable tools for disaster recovery, particularly in war zones.
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References
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