Theme: Geospatial/Spatial

The preprints of these papers are available at ResearchGate.

2026:

A. Thomas, and J. Sreevalsan-Nair, “Strategies for Improving Performance of Attention Mechanism and Late Fusion for Local Climate Zone Classification of Multimodal Satellite Image Data,” in Proceedings of the 2026 IEEE Region 10 Conference (TENCON) (accepted), IEEE, 2026.

A. Thomas, and J. Sreevalsan-Nair, “Hybrid Fusion Model-based Feature Importance Analysis for Local Climate Zone Classification,” in Proceedings of the Fourth International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS 2026) (accepted), IEEE, 2026.

A. Kudupu, A. Priyadarshi, A. Mundayatt, J. Sreevalsan-Nair, A. Verma, and A. Bhattacharya, “Improved Coastline Extraction from SAR Images with Classification-Segmentation Using a CNN-U-Net Two-Stage Architecture,” in Proceedings of the 2026 IEEE SPace, Aerospace and defencCE Conference (SPACE 2026) (accepted), Preprint at https://arxiv.org/abs/2501.12384, IEEE, 2026, p. 6. (Dataset at IEEE DataPort - https://dx.doi.org/10.21227/q8aq-jy35)

A. Mundayatt, and J. Sreevalsan-Nair, FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale, arXiv Preprints, 2026. [Online]. Available: https://arxiv.org/abs/2604.16265.

K. N, A. Kulkarni, and J. Sreevalsan-Nair, “HybriDTVis: 3D Building-Road Reconstruction and Visualization of Hybrid Primitives of City-scale LiDAR Point Clouds for Digital Twins,” in Proceedings of the 12th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2026), INSTICC, 2026, p. 25. (In Press).

K. N, J. Sreevalsan-Nair, and P. Goswami, “Road and Building Reconstruction from 3D LiDAR Point Clouds: Scoping Review,” Archives of Computational Methods in Engineering, p. 36, 2026, https://doi.org/10.1007/s12145-026-02096-9 [Online] Available: https://rdcu.be/fdl6Z; Preprint at https://doi.org/10.21203/rs.3.rs-8743815/v1

A. Thomas, and J. Sreevalsan-Nair, Fusion and Grouping Strategies in Deep Learning for Local Climate Zone Classification of Multimodal Remote Sensing Data, arXiv Preprints, 2026. [Online]. Available: http://arxiv.org/abs/2603.04562

B. B. Desai, Y. A. Rajapur, A. Mundayatt, and J. Sreevalsan-Nair, “CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article),” Earth Science Informatics, vol. 19, no. 36, pp. 1–20, 2026. [Online] Available: https://rdcu.be/e7wxm; Preprint at https://arxiv.org/abs/2510.18878

S. Kothari, M. Srinivasan, S. Kothari, U. Verma, and J. Sreevalsan-Nair, “Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images,” IEEE Access, vol. 14, pp. 22882-22905, 2026, https://doi.org/10.1109/ACCESS.2026.3662436, Preprint at https://arxiv.org/abs/2505.01884

2025:

S. Kothari, M Srinivasan, S. Kothari, U. Verma, and J. Sreevalsan-Nair, Adversarial Robustness of U-Net for Inland Water Body Segmentation from SAR Images, presented at the 13th International Conference on Data Science (IKDD CODS) AI for Sciences Track, 2025.

Y. W. Chit, and J. Sreevalsan-Nair, “Hybrid Deep Learning for Semantic Segmentation of High Resolution Aerial Imagery of Post-Flood Scenes,” in Proceedings of the 2025 IEEE India Geoscience and Remote Sensing Symposium (InGARSS 2025) (in press), IEEE, 2025.

K. K. Htay, and J. Sreevalsan-Nair, “Attention- and Uncertainty-based Enhancements to U-Net Model for Semantic Segmentation of Aerial Imagery for Land Cover Classification,” in Proceedings of the IEEE International Conference on Robotics and Mechatronics (ICRM 2025), IEEE, 2025, pp 1-6. https://doi.org/10.1109/ICRM66809.2025.11349115

J. Sreevalsan-Nair, A. Mundayatt, B. Gnanaraj, A. Thomas, N. C. Kumar, G. G. Sabhahit, S. Joshi, and T. K. Srikanth, “Mental Healthcare in the Times of Climate Change Action and Data Science,” in Data-Driven Insights and Analytics for Measurable Sustainable Development Goals, Elsevier, 2025, pp. 59-82. https://doi.org/10.1016/B978-0-443-33044-5.00010-3. [Online]. Available: Elsevier webpage

S. Kothari, S. Murali, S. Kothari, U. Verma, and J. Sreevalsan-Nair, Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images, arXiv Preprints, 2025. [Online]. Available: https://arxiv.org/abs/2505.01884

J. Sreevalsan-Nair, “Data-Driven Framework for Enhanced Flash Flood Preparedness and Building Urban Resilience,” in Proceedings of the 2025 IEEE Bangalore Humanitarian Technology Conference (B-HTC), pp 1-6, IEEE, 2025. https://doi.org/10.1109/B-HTC64616.2025.11116113

J. Sreevalsan-Nair, and A. Mundayatt, “Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping and Emergence of Deep Learning Methods,”arXiv Preprints, 2025. [Online]. Available: https://arxiv.org/abs/2502.09045

V. Arora, S. Gupta, A. Kudupu, A. Priyadarshi, A. Mundayatt, and J. Sreevalsan-Nair, CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination, arXiv Preprints, 2025. [Online]. Available: https://arxiv.org/abs/2501.12384

A. Moreira, F. Bovolo, J. Sreevalsan-Nair, and G. Ferraioli, “45th IEEE International Geoscience and Remote Sensing Symposium IGARSS 2025, Brisbane, Australia, 3-8 August, 2025: Impressions of the First Days,” IEEE Geoscience and Remote Sensing Magazine, 2025. https://doi.org/10.1109/MGRS.2025.3599367

F. Bovolo, J. Sreevalsan-Nair, A. Plaza, A. Moreira, G. Ferraioli, and C. Persello, “GRSS Awards Presented at the IGARSS 2025 Awards Reception & Ceremony,” IEEE Geoscience and Remote Sensing Magazine, 2025. https://doi.org/10.1109/MGRS.2025.3612752

2024:

D. Katkoria, J. Sreevalsan-Nair, M. Sati, and S. Karunakaran, “WBF-ODAL: Weighted Boxes Fusion for 3D Object Detection from Automotive LiDAR Point Clouds,” in Proceedings of IEEE International Conference on Vehicular Technology and Transportation System (ICVTTS) 2024, IEEE, 2024, 1-6, Best Paper Award. (doi)

V. Jaisankar and J. Sreevalsan-Nair, “SuP-SLiP: Subsampled Processing of Large-scale Static LIDAR Point Clouds,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, ser. GeoSearch ’24, ACM, 2024, 40–47. (doi)

P. Nileshbhai Butani, J. Sreevalsan-Nair, and N. Kamat, “CMA: An End-to-End System for Reverse Engineering Choropleth Map Images,” IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1–5, 2024, also presented at the GRSL Special Stream at the 37th Conference on Graphics, Patterns and Images (SIBGRAPI 2024). (doi)

D. Katkoria, J. Sreevalsan-Nair, M. Sati, and S. Karunakaran, “ME-ODAL: Mixture-of-Experts Ensemble of CNN Models for 3D Object Detection from Automotive LiDAR Point Clouds,” in Deep Learning Theory and Applications, 5th International Conference DeLTA 2024, Dijon, France, July 10-11, 2024, Proceedings, Part II, CCIS, vol. 2172, Springer Cham, 2024, pp. 279–300. (doi)

A. Mundayatt and J. Sreevalsan-Nair, “Scaling up Study Area Size in Flood Susceptibility Mapping,” in Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 2024, pp. 3211–3214. (doi)

J. Sreevalsan-Nair, A. Kiran, A. Bhattacharya, B. D. Sagar, G. KN, U. Verma, K. Lanka, and S. K. Meher, “InGARSS 2023 in Bangalore: Striking a Balance,” IEEE Geoscience and Remote Sensing Magazine, vol. 12, no. 3, pp. 180–187, 2024. (doi)

A. Moreira, F. Bovolo, A. Plaza, and J. Sreevalsan-Nair, “44th IEEE International Geoscience and Remote Sensing Symposium - IGARSS 2024, Athens, Greece, 7-12 July, 2024 Impressions of the First Days,” IEEE Geoscience and Remote Sensing Magazine, vol. 12, no. 3, pp. 149–161, 2024. (doi)

F. Bovolo, J. Sreevalsan-Nair, A. Plaza, H. Yu, and A. Moreira, “GRSS Awards Presented at the IGARSS 2024 Banquet,” IEEE Geoscience and Remote Sensing Magazine, vol. 12, no. 3, pp. 161–170, 2024. (doi)

2023:

L. S. Liang, J. Sreevalsan-Nair, and B. S. D. Sagar, “Multispectral Data Mining: A Focus on Remote Sensing Satellite Images,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1522, October 2023. (doi)(eprint)

L.-T. Tay and J. Sreevalsan-Nair, “Disaster Susceptibility Analysis in Remote Sensing,” in Cognitive Sensing Technologies and Applications, G. R. Sinha, B. Subudhi, C.-P. Fan, and H. Nisar, Eds., Stevenage, UK: Institute of Engineering and Technology (IET), 2023 (doi)(url).

D. Katkoria and J. Sreevalsan-Nair, “Evaluating and Improving RoSELS for Road Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences,” in Deep Learning Theory and Applications: Revised Selected Papers from Third International Conference DeLTA 2022, Portugal, Chapter 6, CCIS volume 1858, Springer Cham, 2023. (doi) (book-link)

J. Sreevalsan-Nair, Co-Association Matrices in Ensemble Clustering: Diverse Applications and Extensions, Preprint available at SSRN, May 2023. (url)

S. Singh and J. Sreevalsan-Nair, “Visual Exploration of LiDAR Point Clouds,” in Advances in Scalable and Intelligent Geospatial Analytics: Challenges and Applications, Chapter 12, K. Kurte, S. Durbha, J. Sanyal, L. Yang, S. Chaudhari, U. Bhangale, and U. Bharambe, Eds., Florida, USA: CRC Press, 2023, p. 19. (doi)

2022:

J. Sreevalsan-Nair, “Interpolation,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Eigenvalues and Eigenvectors,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Independent Component Analysis,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Laplace Transform," in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Expectation-Maximization Algorithm,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Simulated Annealing,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “K-Medoids,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Fuzzy C-Means Clustering," in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Proximity Regression,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Normal Distribution,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Virtual Globe,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “K-Means Clustering,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “K-Nearest Neighbors,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

D. Katkoria and J. Sreevalsan-Nair, “RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence,” in Proceedings of the 3rd International Conference on Deep Learning Theory and Applications (DeLTA), INSTICC, SciTePress, 2022, pp 55–67. ISBN: 978-989-758-584-5. (doi). (Best Paper Award Nomination)

2021:

J. Sreevalsan-Nair, P. Mohapatra, and S. Singh, “IMGD: Image-based Multiscale Global Descriptors of Airborne LIDAR Point Clouds Used for Comparative Analysis,” in Smart Tools and Apps in Graphics (STAG 2021) - Eurographics Italian Chapter Conference, P. Frosini, D. Giorgi, S. Melzi, and E. Rodolá, Eds., The Eurographics Association, October 2021, pp 61--72, ISBN: 978-3-03868-165-6.(doi)

R. Thangavel and J. Sreevalsan-Nair, “CV4FEE: Flood Extent Estimation Using Consensus Voting in Ensemble of Methods for Change Detection in Sentinel-1 GRD SAR Images,” in 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR 2021), IEEE, 2021, pp. 1–6. (doi)

A. C. Victor and J. Sreevalsan-Nair, “Building 3D Virtual Worlds from Monocular Images of Urban Road Traffic Scenes,” in International Symposium on Visual Computing (ISVC 2021), Part II, Lecture Notes in Computer Science LNCS 13018, Bebis, George et al., Springer Nature Switzerland AG, pp 1-14, 2021. (doi)(Easychair preprint)

S. Singh and J. Sreevalsan-Nair, “Adaptive Multiscale Feature Extraction in a Distributed System for Semantic Classification of Airborne LiDAR Point Clouds,” IEEE Geoscience and Remote Sensing Letters, July 2021. (doi)

J. Sreevalsan-Nair, “Maximum Likelihood,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Minimum Entropy Deconvolution,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Data Visualization,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Multiscaling,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “LiDAR,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)

S. Singh, and J. Sreevalsan-Nair, "A Distributed System for Optimal Scale Feature Extraction and Semantic Classification of Airborne LiDAR Point Clouds," in Distributed Computing and Internet Technology, Proceedings of the 17th International Conference on Distributed Computing and Internet Technology (ICDCIT), January 2021, Sequence Number 18, Lecture Notes in Computer Science, Springer International Publishing. (doi)

2020:

S. Singh, and J. Sreevalsan-Nair, "A Distributed System for Multiscale Feature Extraction and Semantic Classification of Airborne LiDAR Point Clouds," accepted at the IEEE International India Geoscience and Remote Sensing Symposium (InGARSS) 2020, pp 74-77, December 2020. (doi) (Best Paper Award)

J. Sreevalsan-Nair, and P. Mohapatra, “Augmented Semantic Signatures of Airborne LiDAR Point Clouds for Comparison,” in arXiv (2020), May 2020, https://arxiv.org/abs/2005.02152

J. Sreevalsan-Nair, and P. Mohapatra, “Influence of Aleatoric Uncertainty on Semantic Classification of Air-borne LiDAR Point Clouds: A Case Study with Random Forest Classifier Using Multiscale Features”, accepted in the Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2020), pp 1066-1070, September 2020. (doi)

2019:

A. C. Victor, and J. Sreevalsan-Nair, "Scene Editing Using Synthesis of Three-Dimensional Virtual Worlds From Monocular Images of Urban Road Traffic Scenes", accepted for spotlight session and poster at the ACM SIGGRAPH European Conference on Visual Media Production (CVMP) in December 2019.(conference-proceedings)

2018:

J. Sreevalsan-Nair, "Visual Analytics of 3D Airborne LiDAR Point Clouds in Urban Regions," in Sarda, N., Acharya, P., Sen, S. (eds) Geospatial Infrastructure, Applications and Technologies: India Case Studies, pp 313-325, Springer Singapore, November 2018. (doi)

J. Sreevalsan-Nair, A. Jindal, and B. Kumari, ``Contour Extraction in Buildings in Airborne LiDAR Point Clouds Using Multi-scale Local Geometric Descriptors and Visual Analytics,'' in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 11(7), pp 2320-2335, June 2018. (doi)

2017:

J. Sreevalsan-Nair, and A. Jindal, ``Using Gradients and Tensor Voting in 3D Local Geometric Descriptors for Feature Detection in Airborne LiDAR Point Clouds in Urban Regions,'' in the Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017), July 2017. (doi)

J. Sreevalsan-Nair, and B. Kumari, ``Local Geometric Descriptors for Multi-Scale Probabilistic Point Classification of Airborne LiDAR Point Clouds,'' in ``Modeling, Analysis and Visualization of Anisotropy,'' Mathematics and Visualization Series, Springer, Cham, pp 175-200, October 2017. (from Proceedings of Dagstuhl Seminar 16142) (doi)

2015:

B. Kumari, and J. Sreevalsan-Nair, ``An interactive visual analytic tool for semantic classification of 3D urban LiDAR point cloud,'' In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems 2015 (p. 73:1--73:4), ACM. (doi)

2014:

B. Kumari, A.Ashe, and J. Sreevalsan-Nair, ``Remote Interactive Visualization of Parallel Implementation of Structural Feature Extraction of Three-dimensional Lidar Point Cloud,'' in the Proceedings of the Third International Conference on Big Data Analytics, Lecture Notes in Computer Science (LNCS) Series, Vol. 8883, 2014, pp 129-132, Springer. (doi)

Page updated

Google Sites

Report abuse