ShoreMan Labs

Research

Research interests at ShoreMan Labs span language technologies, geospatial computing, computer vision, sustainability, and health.

Low-resource NLP

Bengali language processing, sentiment analysis, emotion classification, transliteration, annotation, and evaluation.

Bengali is a morphologically rich language with over 230 million speakers but remains significantly underrepresented in NLP research. Work in this area focuses on building labelled corpora, adapting transformer architectures, and evaluating models for tasks such as sentiment analysis, emotion classification, and rating prediction.

Geospatial Computing

Photogrammetry, 3D reconstruction, spatial data processing, and computational approaches to geographic information.

Geospatial computing spans GIS data workflows, photogrammetric 3D reconstruction, spatial data pipelines, and applications that combine positional information with analytical computation. Interest areas include remote sensing data, point cloud processing, and spatial querying.

Computer Vision

Image-based measurement, visual understanding, and applied computer vision.

Applied computer vision work encompasses image segmentation, object detection, visual measurement, and domain-specific visual analysis. Emphasis is on practical applications where visual data enables quantitative understanding of physical environments.

Sustainability

Applying technology to environmental and resource challenges, with a focus on sustainable approaches to development.

Work in this area explores how spatial data, monitoring systems, and applied software can support sustainable resource management and environmentally-conscious development, particularly in contexts where data infrastructure is limited.

Health Technology

Data-driven approaches to health-sector challenges, including diagnostics, monitoring, and health information systems.

This area focuses on health information systems and data-driven tools that support diagnostics, monitoring, and service delivery, with attention to the practical constraints of resource-limited health settings.