Conference Agenda

Flooding stands as one of the world's prominent natural hazards, which exerts severe threats to sustainable socioeconomic development. Physical asset information in flood disasters refers to the location, quantity, and damage severity of exposed elements within the affected area. Rapid and accurate extraction of such information is crucial for flood disaster emergency management. To achieve this goal, a remote sensing-based framework for extracting physical asset information in flood disasters is proposed in this paper. This framework summarizes extraction methods for flood damage to typical asset types including cropland, buildings, and roads, and comparatively analyzes the advantages and limitations of multi-source remote sensing data, geographic data, and social media data in physical asset information extraction. This study further investigates the differences between statistical analysis, shallow learning methods, deep learning, and transfer learning approaches, with respect to three key dimensions, namely extraction accuracy, scenario applicability, and computational efficiency. Future research should focus on: (1) Development of operational technologies for flood emergency response and disaster mitigation; (2) multi-source data fusion and dynamic simulation based on digital twin technology; (3) intelligent mining of multi-modal information and development of generalized extraction models driven by foundation models, with the aim of providing technical support for rapid flood emergency response.

Rapid flood damage assessment is essential for emergency response and post-disaster recovery. Following catastrophic flooding in the Haihe River Basin on July 28, 2023, the Dongdian flood detention basin was activated on August 1, with inundation persisting until early October. This study integrates satellite remote sensing, UAV imagery, and field surveys to develop a rapid multi-source approach for comprehensive flood loss assessment. The methodology comprises: (1) extraction of inundation characteristics (spatial extent, depth, duration); (2) classification of exposed assets (agricultural land, forests, residential and industrial areas); (3) comprehensive damage and economic loss evaluation. Results show that 301.49 km² (79.55% of the basin) was inundated from August 1 to October 5, 2023, with an average depth of 2.64 m. The central-western zone sustained the most severe damage, with prolonged residential inundation. Complete corn crop failure occurred, and agricultural-forestry production suffered near-total losses. Direct economic losses exceeded 17.5 billion yuan. Compared to traditional field methods, this approach demonstrates superior efficiency and accuracy, providing scientific support for flood risk management in detention basins.

Coastal environments provide various economic, ecological and societal benefits. Coastal erosion which is the gradual loss of sediment over time, poses a significant threat to South Africa’s coastline. The monitoring and detection of coastal erosion is essential for the effective management of coastal environments. One way to quantify coastal erosion is the delineation of coastal boundaries. Remote sensing techniques such as Synthetic Aperture Radar offers a unique opportunity to extract shoreline positions over large areas of the coast. Furthermore, thresholding and edge detection methods have been successfully used to extract land-water boundaries. In this study, C-band SAR data was used to derive backscatter coefficients for three different areas of interest in the Eastern Cape province in South Africa over a ten year period. The coastal erosion and accretion trends were calculated from the results indicated that the Linear Regression Rate (LRR) for the three different study area showed various coastal erosion seasonality trends. The shoreline LLR ranged between -0.01 and -3.28 m/year for the Cape Recife area and -0.17 and -4.78 m/year for the Nelson Mandela Bay beach front. The overall pattern was erosion during the winter months and accretion during the summer months. In contrast, for the Kings Beach area, there was a consistent accretion trend where the LRR values ranged between 0.94 and 1.68 m/year. The findings confirm that SAR remote sensing is suitable for detecting and monitoring coastal changes in three different coastal environments.

Oil spills in confined maritime corridors often evolve faster than any single satellite mission can observe. This often complicates the interpretation of individual images and create gaps in understanding how a spill progresses between satellite overpasses. This study examines whether combining Sentinel-1 and Sentinel-2 observations can provide a more coherent picture of its development of a spill event, using the case of an oil spill occurred near the Strait of Gibraltar in late August 2022 after a collision between the OS35 and the Adam LNG.

The preliminary analysis evaluated each sensor separately. Sentinel-1 highlighted changes in surface roughness, while Sentinel-2 revealed reflectance anomalies linked to modified optical properties of the water. Since neither dataset on its own offered a complete account of the surface conditions, a fusion procedure was applied to the closest pair of post-event images. The fused map displayed sharper boundaries and more spatial detail than the radar scene alone, offering a clearer outline of the affected area. To address the temporal mismatch between acquisitions, intermediate surfaces were also reconstructed for both sensors, producing estimated representations of the marine conditions at dates not directly observed.

Taken together, the fused and reconstructed products formed a more continuous sequence of the spill’s evolution, capturing both its fragmentation and its short-term reorganisation. Although the approach does not replace dedicated operational monitoring, it demonstrates that combining complementary satellite data can reduce ambiguity in single-sensor interpretation and strengthen situational awareness in regions where surface conditions change quickly and unpredictably.

Windstorms in Peninsular Malaysia have increased in both frequency and severity, posing growing risks to communities, infrastructure, and the national economy. Despite these escalating threats, the region currently lacks a comprehensive, location-specific index capable of evaluating and categorizing windstorm hazards for effective planning and mitigation. This study develops a Windstorm Hazard Index (WHI) tailored to Peninsular Malaysia to assess spatial patterns of windstorm risk and support evidence-based decision-making. Four objectives were addressed: (1) identifying key environmental and geographical factors influencing windstorm occurrences; (2) quantifying these parameters using windstorm records from 2008–2018, numerical simulations generated via WRF-ARW, and urban morphology modelling using Envi-MET; (3) formulating the WHI through the integration of Analytic Hierarchy Process (AHP) and Principal Component Analysis (PCA); and (4) validating the index using documented windstorm events from 2020–2024.The WHI categorizes the peninsula into six hazard levels ranging from very low (0.1–0.5) to extreme (0.901–1.0). Southern and central states, including Negeri Sembilan and Pahang, generally exhibited very low hazard levels, while Kelantan and Terengganu showed moderate risk. High-risk zones were concentrated in northern and coastal regions such as Penang, Kedah, and Perlis, with extreme-risk areas detected in parts of Kedah and Perlis. Results indicate that wind speed, temperature, humidity, precipitation, land use, and urban density strongly influence windstorm intensity, particularly in coastal and densely built environments. Validation confirmed the WHI’s reliability, as extreme-risk classifications aligned with recorded damage patterns. Overall, the WHI serves as a robust framework for regional hazard assessment and disaster-resilient infrastructure development across Peninsular Malaysia.

Flooding is one of the most frequent and costliest hydro-meteorological hazards, impacting every nation and causing significant societal and economic disruption. Despite the abundance of Earth Observation (EO) datasets and hydrodynamic models available to map, monitor, and forecast flood events, decision-makers and first responders often struggle to translate these resources into actionable insights. To bridge this gap, we’ve developed the Flood Risk Index for Resilience (FRI-R), a data-driven machine learning model designed to support resource planning, emergency response, and downstream analytics. FRI-R is powered by the Model of Models (MoM), an operational, open-source ensemble framework that integrates outputs from hydrologic models and EO data from optical imagery. Leveraging historical MoM outputs, FRI-R analyzes the spatial and temporal patterns of past flood events and classifies sub-watersheds from high to low risk based on flood frequency and duration, offering a dynamic lens into vulnerability hotspots. MoM has proven effective in disseminating early flood warnings. Building on this success, FRI-R is designed to enable targeted interventions for at-risk populations and critical infrastructures, thereby empowering communities and decision-makers to proactively mitigate and improve long-term resilience.