Conference Agenda

Rapid and reliable assessment of building damage after major earthquakes is essential for effective emergency response and recovery planning. This study formulates post-disaster building damage detection (BDD) as a binary image classification task (damaged vs. undamaged buildings) using multimodal satellite data and a unified ResNet-18 backbone to enable a controlled comparison of fusion strategies. The analysis focuses on the Mw 7.7 Myanmar earthquake of 28 March 2025 and integrates post-event COSMO-SkyMed Second Generation (CSG) dual-polarization (HH, HV) SAR imagery, Maxar optical data, OpenStreetMap (OSM) building footprints, and UNOSAT damage annotations. Three fusion paradigms are evaluated: Early Fusion (EF), Late Fusion (LF), and a novel Middle Fusion (MF) approach. The proposed MF framework introduces a Footprint-Guided Cross-Attention (FGCA) mechanism that uses building geometry as a spatial prior to guide feature-level interaction between SAR and optical representations. Five-fold cross-validation results show that MF consistently outperforms EF and LF, achieving higher precision, F1-score, and robustness across modality configurations. By jointly exploiting SAR structural sensitivity, optical detail, and footprint-based spatial context, the proposed Footprint-Guided Middle Fusion (FGMF) framework enables accurate and scalable building damage mapping from heterogeneous Earth Observation (EO) data.

Accurate and timely building damage detection (BDD) is crucial for disaster emergency response. Although deep learning-based change detection methods have made significant progress in remote sensing, their practical application in disasters still faces two major challenges: (1) Existing high‑accuracy models are typically computationally complex and difficult to deploy for real‑time inference on edge devices.. (2) Model performance heavily relies on large amounts of annotated data, but disaster data are extremely scarce. To address these challenges, this paper proposes a novel lightweight Local‑Global Interaction Network (LGINet) for efficient BDD. The core of LGINet is the proposed Local‑Global Interaction Unit (LGIU), which achieves efficient fusion of detailed and contextual features through a dual‑path architecture and channel‑wise cross‑attention mechanism. Furthermore, a Frequency Difference Enhancement Unit (FDEU) is proposed to generate more accurate damage features, and contrastive learning is employed to reduce the model’s sensitivity to weather conditions and its reliance on annotated data. Experimental results on the xBD and WBD datasets show that LGINet achieves F1-scores of 81.76% and 80.91%, respectively, with an inference speed of 47.83 FPS. It achieves the best balance between accuracy and efficiency, outperforming existing methods.

Recent armed conflicts have increased the need for reliable, spatially explicit damage mapping to support situational awareness, humanitarian assessment, and reconstruction planning. This contribution presents a hybrid change-detection framework for conflict-related urban damage mapping that combines AlphaEarth Foundations embedding change with Sentinel-1 SAR change indices. AlphaEarth provides semantically informed annual embeddings, while Sentinel-1 time series contribute all-weather sensitivity to structural change.

The study compares several embedding-based change metrics and combines the selected AlphaEarth indicator with SAR-derived change measures through simple scalar fusion rules. The proposed framework is designed to preserve the sharp sensitivity of SAR to abrupt structural changes while reducing part of the diffuse background response that often complicates single-source interpretation.

Experiments are conducted over war-affected urban areas in Ukraine, with illustrative examples from Bakhmut and Avdiivka. The results show that AlphaEarth and Sentinel-1 provide complementary information and that their fusion improves the spatial specificity of detected damage patterns. The contribution highlights the potential of combining foundation-model representations with radar time series for operational damage mapping in conflict settings.

Rapid and precise identification of disaster locations is essential for efficient emergency response and management. However, during the immediate post-disaster phase, the lack of timely and reliable information often impedes relief operations. Although satellite imagery and ground-based sensing systems provide valuable data, their effectiveness is constrained by factors such as time delays, high costs, and limited spatial resolution. At the same time, social media platforms such as X (formerly Twitter), Instagram, and Facebook have become valuable channels for real-time, crowd-sourced information. Users function as "human sensors," contributing extensive on-the-ground insights. Much of this content is visual—images that capture the effects of disasters with finer street-level detail and immediacy than textual posts. In this study, we propose a novel, deep learning-based image-matching framework designed to pinpoint the geographic coordinates of disaster events from social media images with street-level accuracy. The core of our approach is to match a query disaster image against a database of georeferenced Google Street View (GSV) imagery. The methodology consists of image pre-processing and feature enhancement; deep feature extraction and matching, and location inference and verification. The preliminary results on an external validation dataset are highly promising, demonstrating a high detection rate of ~90% with confidence scores above 0.9. The model proves resilient to key challenges such as partial occlusion and varied lighting, accurately segmenting multiple objects against complex backgrounds of damaged structures and flooded areas.

The paper presents a novel deep learning framework for automated disaster damage assessment using remote sensing imagery. It addresses the challenge of timely and accurate damage classification in the aftermath of disasters, aiming to improve emergency response and resource allocation. The proposed system leverages both pre- and post-disaster satellite images to assess building damage across four categories: no damage, minor damage, major damage, and destroyed. The central innovation lies in the development of a multi-modal attention mechanism, which integrates features from both pre- and post-event images to enhance damage detection. A lightweight ConvNeXT-Tiny architecture serves as the backbone, ensuring efficient processing while maintaining high performance.

Key contributions of this work include: (1) a cross-attention module that fuses multi-modal data, (2) an optimized preprocessing pipeline designed for large-scale datasets, and (3) novel data augmentation techniques that improve the model’s robustness. Experiments on a large-scale disaster damage dataset show the model achieves an impressive 94.90% classification accuracy, with strong performance in discriminating damage levels and resilience to incomplete or corrupted data. This framework represents a significant step forward in disaster response, offering a scalable solution for real-time damage detection. The research demonstrates the potential of combining remote sensing, multi-temporal imagery, and deep learning to expedite and improve disaster damage assessment, ultimately supporting more efficient emergency management.

Urban areas evolve rapidly, often increasing exposure to natural hazards, especially in seismically active regions such as southern Turkey. This contribution presents an AI-based workflow for multi-temporal analysis of urban expansion in the city of Osmaniye between 2015 and 2025. The methodology integrates Sentinel-2 multispectral imagery with a U-Net convolutional neural network trained on World Settlement Footprint (WSF) masks for binary segmentation of built-up versus non-built-up areas. After training on 2015 and 2019 data, the model was applied to the full temporal series to assess its generalisation capability and to quantify long-term urban growth.

Results show a substantial increase in built-up surfaces over the decade, with a temporary decline linked to the 2023 earthquake and a marked acceleration during the reconstruction phase. Beyond the quantitative trends, the spatial patterns identified by the model highlight how urban expansion has progressively shifted from the central districts toward peripheral zones, revealing both densification processes and outward sprawl. These observations provide valuable indications on how development pressures interact with seismic vulnerability.

The approach demonstrates the potential of AI and open satellite data for large-scale, reproducible monitoring of urban dynamics and for supporting risk-informed urban planning. Because it relies entirely on open-source datasets and tools, the workflow can be easily transferred to other hazard-prone regions, offering a scalable and transparent framework for assessing urban change, post-disaster reconstruction, and long-term exposure.