By Dr Rabindra Lamsal

Book Chapters

Lamsal R; Kumar TVV, 2020, 'Artificial Intelligence and Early Warning Systems', in AI and Robotics in Disaster Studies, Springer Nature, pp. 13 - 32, http://dx.doi.org/10.1007/978-981-15-4291-6_2

Lamsal R; Vijay Kumar TV, 2020, 'Artificial Intelligence Based Early Warning System for Coastal Disasters', in Disaster Research and Management Series on the Global South, Springer Singapore, pp. 305 - 320, http://dx.doi.org/10.1007/978-981-15-4294-7_21

Journal articles

Lamsal R; Zlatanova S, 2026, 'Query2Property: Semantic retrieval of IFC properties for natural language BIM queries', ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 11, pp. 323 - 330, http://dx.doi.org/10.5194/isprs-annals-XI-4-2026-323-2026

Pan Y; Wang M; Lu L; Lamsal R; Pärn E; Zlatanova S; Brilakis I, 2026, 'LLM-enabled multi-agent framework for natural language interaction with graph-based digital twins', Automation in Construction, 183, http://dx.doi.org/10.1016/j.autcon.2026.106791

Lamsal R; Read MR; Karunasekera S; Imran M, 2025, 'CReMa: Crisis Response Through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media', IEEE Transactions on Computational Social Systems, 12, pp. 306 - 319, http://dx.doi.org/10.1109/TCSS.2024.3453226

Lamsal R; Read MR; Karunasekera S, 2024, 'CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts', Knowledge Based Systems, 296, http://dx.doi.org/10.1016/j.knosys.2024.111916

Lamsal R; Read MR; Karunasekera S, 2024, 'Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts', Proceedings of the International ISCRAM Conference, http://dx.doi.org/10.59297/zxa19c16

Lamsal R; Read MR; Karunasekera S, 2023, 'BillionCOV: An enriched billion-scale collection of COVID-19 tweets for efficient hydration', Data in Brief, 48, http://dx.doi.org/10.1016/j.dib.2023.109229

Lamsal R; Harwood A; Read MR, 2023, 'Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey', ACM Computing Surveys, 55, http://dx.doi.org/10.1145/3524498

Lamsal R; Harwood A; Read MR, 2022, 'Twitter conversations predict the daily confirmed COVID-19 cases', Applied Soft Computing, 129, http://dx.doi.org/10.1016/j.asoc.2022.109603

Lamsal R, 2021, 'Design and analysis of a large-scale COVID-19 tweets dataset', Applied Intelligence, 51, pp. 2790 - 2804, http://dx.doi.org/10.1007/s10489-020-02029-z

Lamsal R; Katiyar S, 2020, 'cs-means: Determining optimal number of clusters based on a level-of-similarity', SN Applied Sciences, 2, http://dx.doi.org/10.1007/s42452-020-03582-5

Conference Papers

, 2023, 'A Twitter Narrative of the COVID-19 Pandemic in Australia', in Proceedings of the International ISCRAM Conference, Information Systems for Crisis Response and Management, presented at Proceedings of the 20th International Conference on Information Systems for Crisis Response and Management, http://dx.doi.org/10.59297/gqed8281

Lamsal R; Harwood A; Read MR, 2022, 'Where did you tweet from? Inferring the origin locations of tweets based on contextual information', Institute of Electrical and Electronics Engineers (IEEE), pp. 3935 - 3944, presented at 2022 IEEE International Conference on Big Data (Big Data), http://dx.doi.org/10.1109/bigdata55660.2022.10020460

Lamsal R; Harwood A; Read MR, 2022, 'Addressing the location A/B problem on Twitter', Association for Computing Machinery (ACM), pp. 1 - 4, presented at Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising, http://dx.doi.org/10.1145/3557992.3565989

Preprints

Lamsal R; Zlatanova S; Shen JX, 2026, CityLLM: A framework for natural-language querying of semantic 3D city models, https://arxiv.org/abs/2607.14542v1

Lamsal R; Zlatanova S; Xu H; Sun Y; Shen JX, 2026, IfcLLM: Natural Language Querying of IFC Models through Complementary Relational and Graph Representations, https://arxiv.org/abs/2605.13236v2

Lamsal R; Read MR; Karunasekera S, 2025, Langformers: Unified NLP Pipelines for Language Models, https://arxiv.org/abs/2504.09170v1

Lamsal R; Read MR; Karunasekera S; Imran M, 2025, "Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts, https://arxiv.org/abs/2502.16839v1

Lamsal R; Read MR; Karunasekera S; Imran M, 2024, CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media, https://arxiv.org/abs/2405.11897v2

Lamsal R; Read MR; Karunasekera S, 2024, Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts, https://arxiv.org/abs/2403.16614v1

Lamsal R; Read MR; Karunasekera S, 2023, CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts, https://doi.org/10.1016/j.knosys.2024.111916

Lamsal R; Read MR; Karunasekera S, 2023, A Twitter narrative of the COVID-19 pandemic in Australia, https://doi.org/10.59297/GQED8281

Lamsal R; Read MR; Karunasekera S, 2023, BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration, https://doi.org/10.1016/j.dib.2023.109229

Singh P; Lamsal R; Monika ; Chand S; Shishodia B, 2023, GeoCovaxTweets: COVID-19 Vaccines and Vaccination-specific Global Geotagged Twitter Conversations, https://arxiv.org/abs/2301.07378v1

Lamsal R; Harwood A; Read MR, 2022, Where did you tweet from? Inferring the origin locations of tweets based on contextual information, https://doi.org/10.1109/BigData55660.2022.10020460

Lamsal R; Harwood A; Read MR, 2022, Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey, https://doi.org/10.1145/3524498

Lamsal R; Harwood A; Read MR, 2022, Twitter conversations predict the daily confirmed COVID-19 cases, https://doi.org/10.1016/j.asoc.2022.109603

Lamsal R; Katiyar S, 2018, Determining Optimal Number of k-Clusters based on Predefined Level-of-Similarity, https://doi.org/10.1007/s42452-020-03582-5

Lamsal R; Choudhary A, 2018, Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning, https://arxiv.org/abs/1809.09813v5

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