Peer-reviewed work on conformal prediction for vision-language models, cultural AI benchmarks for Southeast Asia, and geospatial deep learning for flood and mining detection. Published in IEEE, ACL, and Remote Sensing of Environment.
Q1 · IF: 11.4
Multi-modal deep learning approaches to semantic segmentation of mining footprints with multispectral satellite imagery
Semantic segmentation of global mining footprints using multispectral satellite imagery across 37 locations worldwide.
Remote Sensing of Environment, Volume 318, 2025Read Paper
Existing remote sensing applications in mining are often of limited scope, typically mapping multiple mining land covers for a single mine or only mapping mining extents or a single feature (e.g., tailings dam) for multiple mines across a region. Many of these works have a narrow focus on specific mine land covers rather than encompassing the variety of mining and non-mining land use in a mine site. This study presents a pioneering effort in performing deep learning-based semantic segmentation of 37 mining locations worldwide, representing a range of commodities from gold to coal, using multispectral satellite imagery, to automate mapping of mining and non-mining land covers. Due to the absence of a dedicated training dataset, we crafted a customized multispectral dataset for training and testing deep learning models, leveraging and refining existing datasets in terms of boundaries, shapes, and class labels. We trained and tested multimodal semantic segmentation models, particularly based on U-Net, DeepLabV3+, Feature Pyramid Network (FPN), SegFormer, and IBM-NASA foundational geospatial model (Prithvi) architecture, with a focus on evaluating different model configurations, input band combinations, and the effectiveness of transfer learning. In terms of multimodality, we utilized various image bands, including Red, Green, Blue, and Near Infra-Red (NIR) and Normalized Difference Vegetation Index (NDVI), to determine which combination of inputs yields the most accurate segmentation. Results indicated that among different configurations, FPN with DenseNet-121 backbone, pre-trained on ImageNet, and trained using both RGB and NIR bands, performs the best. We concluded the study with a comprehensive assessment of the model performance based on climate classification categories and diverse mining commodities.
MAJOR CONTRIBUTOR · ACL 2025
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
A multicultural vision-language benchmark for Southeast Asia, covering 1.28M culturally relevant images across 11 SEA languages.
Despite Southeast Asia's extraordinary linguistic and cultural diversity, the region remains significantly underrepresented in vision-language research. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing culturally relevant high-quality datasets for SEA languages.
FIRST AUTHOR · IEEE Q1
Progressive Cross-Attention Network for Flood Segmentation Using Multispectral Satellite Imagery
ProCANet achieving state-of-the-art IoU of 0.815 on the Sen1Floods11 benchmark.
IEEE Geoscience and Remote Sensing Letters, 2024Read Paper
We introduce a progressive cross-attention network (ProCANet) that progressively applies both self- and cross-attention mechanisms to multispectral features, generating optimal feature combinations for flood segmentation.
Q2 · IF: 2.3
Enhancing urban resilience through integrated flood policy and planning
Mixed-methods evaluation of retention ponds for urban flood mitigation.
AQUA - Water Infrastructure, Ecosystems and Society, Volume 74(2), 2025Read Paper
This study examines the role of retention ponds in South Bandung as a strategic response to flood management challenges.
ACL 2026
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
A community-driven human-annotated benchmark covering 109 languages.
We introduce Anthropogenic Regional Adaptation: a novel paradigm that aims to optimize model relevance to specific regional contexts while ensuring the retention of global generalization capabilities.
FIRST AUTHOR
The Effect of Plastic Bag Ban Policy Towards Waste Complaints in Jakarta
RESEARCH ARCHIVE