http://repo.lib.sab.ac.lk:8080/xmlui/handle/123456789/1719 Journal of Geospatial Surveying 2026-03-07T08:02:08Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/123456789/1723 GIS based Approach for Planning the Evacuation Process During Flash Floods: Case Study for Gampaha Divisional Secretariat Division, Sri Lanka Edirisinghe, E.A.K.R; Pussella, P.G.R.N.I.; Vidarshana, W.D.M Gampaha district is one of the areas that suffer from frequent flash floods in Sri Lanka. Since flash floods occur unexpectedly, and with minimal warning, preparedness is essential to minimize losses during such a disaster. Planning the evacuation during a flood is a complex process; therefore, it needs focused consideration on several factors. The main objective of this paper is to propose a Geographical Information Systems (GIS) based approach to plan the evacuation process during a situation where there is a flash flood in the Gampaha Divisional Secretariat Division (DSD), with the intent to reduce negative consequences. The study has considered seven criteria: elevation, accessibility, land-use, availability of buildings, presence of water features, rainfall, and population density, in selecting locations for evacuation centers. These data were analyzed with the tools and models available in the GIS software package. As a first step, the flood inundation map was created using elevation and rainfall data. Evacuation centers were then identified outside of the inundated area. Finally, after field verification, 7 potential locations (Bandaranayake Vidyalaya, Bandarawatta Parakrama Vidyalaya, Sri Sumangalaramaya, Madegama Sri Sunandaaramaya, Sri Wajiraghanaramaya, St. Jude Church Idigolla, and Holy Cross College) were selected by considering the capacities such as elevation (above 15m from the Mean Sea Level), accessibility (within 200m from main roads), ownership (public only), and the number of people accommodated. The results of this study will be very helpful for the government, non-government organizations, and the victims to take immediate actions during a flash flood event in the study area. 2021-05-31T00:00:00Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/123456789/1722 A Grid-based Automated Building Extraction Technique for Low-cost UAV Images Gamage, R.R.; Nalani, H.A With increasing urbanization, new technology is required to fulfil both human and environmental needs. At present, low-cost UAVs are used in surveying and mapping, and during the past few years, they have reached a level of practical requirements to allow the use of these systems as mapping platforms. Moreover, UAV based mapping provides required accuracy in line with cadastral laws and policies. Extraction of urban objects is a pre-requisite in various applications. In general, detection of buildings plays a major role in the field of remote sensing image processing, and also in urban planning and management. However, there is no ‘proper’ method developed to detect building features automatically from UAV images because there are usually too many details and distortions on the images. This paper presents an effective approach for extracting buildings from UAV images through the incorporation of orthophotographs and dense point clouds, rather than the traditional pixel based classification. In this method, different feature-based conditions are introduced with the help of a grid-based data structure for more accurate and quick extraction of building features. To verify the generality and advantage of the proposed method, the procedure is evaluated by performing experiments with a dataset acquired over the study area, which has a variety of building patterns and styles. The experimental results show an excellent performance in the detection of buildings, with an average overall accuracy greater than 80%. The final overall correctness and quality of building extraction are more than 80% and 65%, respectively. Therefore, there is a need to focus on more advanced conditions for building detection, to obtain optimum results. 2021-05-01T00:00:00Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/123456789/1721 Crowdsourced Data Relevance Analysis for Crowd-assisted Flood Disaster Management Kaushalye, N.A.V.O.; Koswatte, S The recent climate changes have significantly increased the number and intensity of natural disasters around the world. This includes floods that cause a great deal of damage to properties, and more importantly, to the lives of the people. The reporting of current disasters has changed from official media to public reporters through social media and crowdsourcing technologies which have guaranteed the availability and up-to-date nature of the reported data. However, crowdsourced data (CSD) are often questioned due to issues in reliability and relevancy, heterogeneity or bias, bad structure, and un-professionalism. As a result of this, disaster responders are reluctant to use such data for their critical decision making actions. Using Natural Language Processing (NLP) and Geographic Information Retrieval (GIR) techniques, this study evaluated the quality of CSD, focusing on the thematic relevance. The study examined a proof of concept on relevance assessment based on an improved set of user queries utilizing crowdsourced messages from the 2011 Australian floods (Ushahidi Crowd-map). The findings show that the approach was effective in generating a thematically rated list of CSD messages for post-flood disaster managers to confidently take actions. The study's future work will consider thematic and geographic specificities and semantic context of the modified queries. Moreover, it is expected to test the approach with similar geospatial crowd-map data, and finally to check the possibility of integrating the derived information with authoritative datasets such as Spatial Data Infrastructures (SDIs). 2021-05-31T00:00:00Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/123456789/1720 Lognormal Random Field models to identify temporal Land cover changes using full polarimetric L-Band SAR imagery Welikanna, D.R; Tamura, M Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references. 2021-05-31T00:00:00Z