International Conference on Technology Innovations for Crisis Management [ICTICM]
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Item Open Access Automated Flood Impact Detection from Remote Sensing Data using Transfer Learning: A New Zealand Case Study(Sri Lanka Institute of Information Technology, 2026-05-21) Pathirana, N; Ranasinghe, M; Prasanna, RProper flood damage assessment is required for rapid emergency response and recovery planning. Highresolution drone imagery provides greater visual detail compared to satellite imagery and can support more accurate damage assessment after flood events. This work-in-progress study investigates the use of transfer learning with a pre-trained ResNet50 convolutional neural network to classify post-flood drone images. A manually annotated dataset of drone images representing damaged and non-damaged areas was used to finetune the pre-trained model and evaluate its performance using several metrics. The training process employed hyperparameter tuning to select the best model which achieved an accuracy of approximately 87%. These preliminary results demonstrate the potential of transfer learning for flood damage classification using a limited drone image dataset. Future work will focus on comparing the performance of additional deep learning models, incorporating satellite imagery and extending the approach from image-level classification to object-level damage detection.Item Open Access Spatial Modelling of Cyclone-Induced Flood Hazards for Disaster Risk Financing and Parametric Insurance in Sri Lanka(Sri Lanka Institute of Information Technology, 2026-05-21) Pathiraja,S.; Wanniarachchi, S; Weerakoon, K.; Pramudi, L.; Wanigasooriya, D.; Pathirana, A.; Perera, T.; Hettiarachchi, E.; Sayakkara, A.; Goonetillake, J.Cyclone-induced compound hazards present a critical challenge for disaster risk financing (DRF) in South Asia, yet spatially-explicit and probabilistically-calibrated hazard models suitable for parametric insurance applications remain largely absent in Sri Lanka. This work-in-progress paper presents an integrated research protocol for developing and evaluating spatially-informed parametric insurance models for cyclone-related hazards. The framework combines GIS-based Fuzzy Analytic Hierarchy Process (FAHP) multi-hazard mapping, coupled with HEC-RAS 1D/2D hydrological-hydraulic simulation, Monte Carlo Probabilistic Cyclone Hazard Analysis (PCHA), and spatial lag and spatial error econometric modeling. This study focuses on five cyclone-affected districts of Sri Lanka, with Kurunegala serving as the primary analytical case study, which collectively displaced more than 95,000 people during Cyclone Ditwa (Nov–Dec 2025). This study will produce Grama Niladhari (GN)-division-level flood inundation maps, probabilistic wind speed and rainfall exceedance surfaces for return-period pricing, empirical basis risk hotspot maps, and Willingness-to-Pay (WTP) estimates. Findings will directly inform parametric trigger design, actuarial premium calibration, and sovereign disaster risk financing strategies for climate-vulnerable developing countries.Item Open Access Automated Landslide Mapping with Open-Source Satellite Data in GEE: A Sri Lankan Case Study.(Sri Lanka Institute of Information Technology, 2026-05-21) Lasantha H S; Prasanna R; Madusanka D A GRainfall-induced landslides are the most frequent natural hazards in Sri Lanka. This study developed a scalable, cloudbased landslide detection method that integrates satellite data, topographic parameters, and a machine learning framework implemented on Google Earth Engine (GEE) to support rapid, reliable landslide mapping in disaster scenarios and datascarce regions. The methodology was applied to the central highlands and the south-western parts of Sri Lanka. Multitemporal Sentinel-1 and Sentinel-2 images, along with a 12.5 m digital elevation model, were used to generate four composite datasets (M1–M4) for pixel-based classification. M1 includes spectral index differences between pre-event and post-event optical images, M2 incorporates spectral distance and spectral angle data, M3 contains topographic attributes, and M4 combines all datasets with synthetic aperture radar (SAR) amplitude ratio bands. Inter-area model training and validation were conducted using RF, CART, SVM, and GTB with independent datasets from study area-1, followed by transferability testing in a second area. Model performance was evaluated using overall accuracy, Precision, Recall, F1- Score, Kappa, and AUC. SVM-M4 performed best in interarea analysis, while GBT–M4 and CART–M3 showed supe Cloud contamination and false detections remained key challenges.Item Open Access Testing of a resilient community-driven communications network of LoRa devices in Wellington(Sri Lanka Institute of Information Technology, 2026-05-21) Perera, W.M.M; Connor-Kebbell, T; Abeydeera, T; Rallapudi, M.O; Vautier, A; Ngan,B; Prasanna, RModern communications infrastructure such as mobile base stations, routing centres and servers are highly vulnerable to grid power loss and catastrophic damage that occur during natural disasters. Such failures render critical communications infrastructure inoperable exactly when they are needed. To address this, CRISiSLab has deployed and evaluated a Meshtastic LoRa mesh network across Wellington’s varied terrain as a low-power, infrastructure independent communications solution. Testing demonstrated that while terrain was the primary limiting factor, effective city-scale communications is achievable with strategic device placement and appropriate preset selection. These findings suggest that Meshtastic is viable as an emergency backup communication network.Item Open Access Securing Distributed Disaster Early Warning Networks(Sri Lanka Institute of Information Technology, 2026-05-21) Senarathne, K; Dias, D; Prasanna, RThis paper proposes a secure and cost-effective communication and management approach for distributed disaster early warning networks to ensure reliable, low-latency alert dissemination. While decentralized architectures reduce latency by enabling direct device communication, they are highly vulnerable to attacks due to their exposure in public community settings and the challenges posed by Network Address Translation (NAT) and firewalls. Traditional NAT traversal techniques often lack the built-in security required for such sensitive infrastructure. We identify Nebula, an open-source overlay networking tool, as a viable solution because it is lightweight enough for low-resource devices such as the Raspberry Pi typically found in decentralized early warning systems, supports NAT traversal, and provides endto- end encryption. Preliminary experiments conducted in an AWS environment—designed to simulate real-world Raspberry Shake sensor deployments—confirm Nebula’s ability to establish direct peer-to-peer (P2P) connectivity with a single hop and effectively encrypt trafficItem Open Access From Beach to Cliff: Adapting CoastSnap Citizen Science for Coastal Cliff Change Detection Using ML-Assisted Image Registration and Prompted Segmentation(Sri Lanka Institute of Information Technology, 2026-05-21) aramillo-Velez, A; Gamlath, S; Chandirakumar, M; Prasanna, R; de Vilder,S; McColl, S; Tan, M.L; Stewart, C; Ambegoda, T.DCoastSnap is a citizen science tool that uses repeat smartphone photographs from fixed stations to monitor coastal change, yet it has rarely been applied to coastal cliffs. We test a workflow for cliff change screening using a controlled pilot CoastSnap station at Ōnaero, New Zealand (iPhone 12), integrating (i) Machine Learningassisted ground control point (GCP) transfer for image registration, (ii) pinhole camera calibration and reprojection-based geo-rectification onto a curved cliff-surface model, and (iii) prompted segmentation for isolating cliff-related features. Auto-GCP benchmarking shows a tradeoff between precision and robustness: the Scale-Invariant Feature Transform (SIFT) model achieves low localisation error when successful, but is sensitive to changes in illumination. In contrast, the Local Feature ransformer (LoFTR) model provides a higher detection yield and more stable performance, but is sensitive to the threshold used. Nevertheless, it is well suited to operational use with human-inthe- loop verification. Calibration produced consistent parameters across repeat images, with focal length estimates matching device specifications. Visual segmentation based only in Regions of Interest (RoI) often merged adjacent objects, while text-guided Segment Anything Model (LangSAM) delineated cliff and debris more reliably than fracture-like features. Preliminary detection of the supply and removal of debris highlights the potential for quantifying the frequency and magnitude of mass movements at each cliff.Item Open Access A Dynamic LUCIS Framework for Identifying Urban-Hazard Conflicts in the Kelani River Basin Using Near Real-Time Earth Observation Data(Sri Lanka Institute of Information Technology, 2026-05-21) Aloka, O; Nayanajith, B; Dassanayake, SRapid urbanisation in Sri Lanka frequently bypasses formal planning, resulting in concealed land-use conflicts where infrastructure encroaches on disaster-prone areas. Traditional Land-Use Conflict Identification Strategy (LUCIS) models are based on static, outdated survey data and fail to capture realtime risks. This temporal lag leads to undetected unauthorized settlements in hazard zones increasing disaster vulnerability. This study introduces a Dynamic LUCIS framework utilizing near real-time Earth Observation (EO) data from Google Earth Engine, specifically the Dynamic World and Open Buildings datasets. Focused on the Kelani River Basin, the model identifies critical risk conflicts between urban development and flood/landslide safety zones. Validation using high-resolution drone imagery yielded an overall accuracy of 82.84%, F1-score of 0.83 and a Kappa coefficient of 0.66. The results demonstrate that EO-driven models can identify unauthorized settlements in hazard-prone areas with high precision, providing a scalable tool for resilient urban governance.Item Open Access Voice Communication over Meshtastic Networks(Sri Lanka Institute of Information Technology, 2026-05-21) Uduwaka, S; Gunawardana, W M T V; Lakshan, W D T; Pathirana, R P S; Prasanna,R; Dias, D; Gayan, SVoice communication is essential in post-disaster scenarios where users may be unable to type due to injury, stress, or environmental constraints. While Meshtastic provides a decentralized LoRa-based mesh network for long-range messaging, it does not natively support voice and is limited by low data rates and strict regulatory duty-cycle requirements. This paper investigates two methods for enabling voice over such networks: waveform-based audio packetization and a semantic speech-to-text (STT) / text-to-speech (TTS) approach. Preliminary experimental evaluations demonstrate that waveform-based methods, even when using low-bitrate codecs like Codec2, are impractical for urgent communication; delivering a 3-second voice message requires hundreds of packets and several minutes due to ransmission latency and duty-cycle constraints. In contrast, the proposed offline STT/TTS method transcribes speech locally, transmits it as a standard text payload, typically in a single packet and reconstructs the audio at the receiver. This semantic approach reduces delivery time from minutes to seconds, while maintaining high reliability and regulatory compliance. The study concludes that semantic communication is the most viable solution for voice interaction in bandwidth-constrained LoRa mesh environments.Item Open Access Development of a MEMS-based Earthquake Dataset using the Raspberry Shake Network in New Zealand(Sri Lanka Institute of Information Technology, 2026-05-21) Samiha, T.Z; Ravishan, DTraditional seismic monitoring is often limited by the high cost of instrumentation and logistical barriers, hindering the expansion of earthquake monitoring networks especially in under-resourced regions. Low-cost MEMS-based sensors offer a scalable alternative, but require specialized datasets to train machine learning models adapted to their unique noise characteristics and sensitivity profiles. To address this, we systematically collected waveforms from approximately 4,000 earthquakes (magnitude 2.7 to the highest recorded) recorded across 89 Raspberry Shake stations in New Zealand from 2020–2025. Events were matched to nearby stations based on epicentral distance criteria (100 km for M 2.7–5.5, 150 km for M>5.5). A staged filtering pipeline using PhaseNet, EQTransformer, and GPD models, cross-validated with theoretical TauP arrivals, was applied to ensure phase pick quality across three confidence tiers. The final curated dataset comprises 918 high-confidence waveforms with validated P and S wave arrivals, alongside approximately 16,035 total waveform records spanning all quality tiers. This dataset addresses the critical scarcity of labeled training data for low-cost seismic instrumentation, enabling the development of phase pickers specifically calibrated for MEMS sensors.Item Open Access Large Language Model Pipelines for Crisis Intelligence: Automated Help Request Classifier(Sri Lanka Institute of Information Technology, 2026-05-21) De Silva, L.D.R.E.This research examines the development and deployment of a Crisis Intelligence Pipeline, engineered to address the catastrophic information surge following Cyclonic Storm Ditwah in Sri Lanka [1]. The system integrates advanced prompt engineering methodologies, specifically few-shot learning, Chain-of-Thought [2], and Tree-of-Thoughts [3] to transform unstructured humanitarian data into actionable logistics strategies. Through rigorous stability testing under varying temperature gradients, the study identifies a "Safe Mode" for deterministic triage and a "Chaos Mode" that highlights the risks of model hallucination in high-stakes environments. Furthermore, the pipeline utilizes Pydantic-based schema validation [4] and token economics [5] to ensure scalability and cost-efficiency. Results indicate that structured reasoning architectures significantly enhance the precision of resource allocation and signal-to-noise discrimination, providing a vital technological framework.