Published in MethodsX, 2025
Recommended citation: Niroshan, L., Moslem, S., Pilla, F., 2025. Design and implementation of a data sharing API for supporting urban governance schemes in environmental and traffic monitoring. MethodsX. https://doi.org/10.1016/j.mex.2025.103458
Download Here: https://www.sciencedirect.com/science/article/pii/S2215016125003036
Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025
Recommended citation: Niroshan, L., Carswell, J., 2025. DeepMapper: A GeoAI Approach to Automate the VGI Mapping Workflow. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2025.3581499
Download Here: https://ieeexplore.ieee.org/abstract/document/11045930
Published in ISPRS International Journal of Geo-Information, 2025
Recommended citation: de Sena, Í., Niroshan, L., Rosecký, J., Brůža, V., Butler, M., Cocco, C., 2025. A Playful Participatory Planning System (P-PPS): A Framework for Collecting and Analyzing Player-Generated Spatial Data from Minecraft Worlds. ISPRS International Journal of Geo-Information. https://doi.org/10.3390/ijgi14060210
Download Here: https://www.mdpi.com/2220-9964/14/6/210
Published in Lecture Notes in Computer Science, 2023
This paper introduces Poly-GAN, a data-driven approach utilizing Deep Learning to regularize irregular building footprints from OpenStreetMap data. Poly-GAN aims to optimize polygonal representations of buildings for digital maps, addressing limitations of traditional methods in accuracy and efficiency.
Recommended citation: Niroshan, L. and Carswell, J.D., 2023, June. Poly-GAN: Regularizing Polygons with Generative Adversarial Networks. In International Symposium on Web and Wireless Geographical Information Systems (pp. 179-193). Cham: Springer Nature Switzerland.
Download Here: https://lasith-niro.github.io/files/polygan.pdf
Published in International Conference on Geoinformatics and Data Analysis, 2022
This paper introduces OSM-GAN, a novel approach for detecting spatial changes in high-resolution satellite images. The model leverages generative adversarial networks to identify building footprint changes in OpenStreetMap data, demonstrating its potential for monitoring urban development.
Recommended citation: Niroshan, L. and Carswell, J.D., 2022. OSM-GAN: Using Generative Adversarial Networks for Detecting Change in High-Resolution Spatial Images. In International Conference on Geoinformatics and Data Analysis (pp. 179-193). Springer, Cham.
Download Here: https://lasith-niro.github.io/files/osm-gan.pdf
Published in AGILE: GIScience Series, 2022
This paper explores using Ireland's national supercomputer, Kay, to train Deep Learning models for remote sensing tasks, specifically extracting buildings from satellite imagery. Leveraging Kay's GPU clusters showcases significant advancements in computational efficiency for handling large-scale data in this domain.
Recommended citation: Niroshan, L. and Carswell, J.D., 2022. Machine learning with kay. AGILE: GIScience Series, 3, p.11.
Download Here: https://lasith-niro.github.io/files/ml-with-kay.pdf
Published in ISPRS International Journal of Geo-Information, 2022
This paper presents a post-analysis of OSM-GAN, a generative adversarial network for spatial change detection in OpenStreetMap data. The study evaluates the model's performance in detecting building footprint changes over time, demonstrating its potential for monitoring urban growth and development.
Recommended citation: Niroshan, L. and Carswell, J.D., 2022. Post-analysis of OSM-GAN spatial change detection. ISPRS International Journal of Geo-Information, 11(5), p.295Niroshan, L., Carswell, J.D. (2022). Post-analysis of OSM-GAN Spatial Change Detection. In: Karimipour, F., Storandt, S. (eds) Web and Wireless Geographical Information Systems. W2GIS 2022. Lecture Notes in Computer Science, vol 13238. Springer, Cham. https://doi.org/10.1007/978-3-031-06245-2_3
Download Here: https://lasith-niro.github.io/files/post-osm-gan.pdf
Published in TUDublin Annual Research Symposium, 2020
This poster summarizes the proposed research on DeepMapper, a Deep Learning-based approach for automatic updating of crowdsourced maps. The study aims to develop a model that can detect and update changes in OpenStreetMap data, facilitating the maintenance of digital maps through automation.
Recommended citation: Hewa Manage, Lasith Niroshan and Carswell, James, 'DeepMapper : Automatic Updating Crowdsourced Maps' (2020). Other. 3. https://arrow.tudublin.ie/gradcamoth/3
Download Here: https://lasith-niro.github.io/files/deepmapper-poster.pdf