International Conference on Technology Innovations for Crisis Management [ICTICM]

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    ItemOpen 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 G
    Rainfall-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.
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    ItemOpen 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.D
    CoastSnap 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.