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    Assessing urban growth and pollution throughnightlight data: a case study in Thailand: Linking urban growth and CO concentrations via nightlights
    (2026-04-30)
    Kulworatit, Chaichana
    ;
    Kerdpramote, Phuvis
    ;
    Saetang, Saranya
    This study explores the relationship between urban development and air pollution in Thailand by analyzing remote sensing nightlight data and carbon monoxide (CO) concentrations over six years (2019-2024). Using data from VIIRS Day/Night Band (DNB) satellite imagery, CO levels, electricity consumption, and lignite production, the study finds a significant positive correlation (Pearson coefficient = 0.586) between nightlight intensity and CO concentrations. This suggests that nightlight data can be an effective tool for monitoring urban-related pollution. Seasonal and regression analyses show that urban growth contributes to pollution, but this is influenced by seasonal patterns and energy consumption. Multiple regression models highlight nightlight intensity as the strongest predictor of CO levels, with energy factors adding significant explanatory power. Regional analysis identifies the Bangkok Metropolitan Region as having the highest nightlight intensity and CO levels (correlation = 0.598). Lag correlation analysis suggests that changes in CO and nightlight intensity are most strongly correlated at zero lag, with CO changes slightly leading in some areas. These findings have implications for urban planning, environmental policy, and public health in Southeast Asia.
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    Cross-Domain Robust Liveness Detection: A Transfer Learning Approach for Combating Sophisticated Presentation Attacks in Mobile Authentication
    (2025-01-01)
    Kerdpramote, Phuvis
    ;
    Poomdeesittinon, Akeanant
    ;
    Jamsri, Rapeeploy
    ;
    Krueyos, Phanuwit
    ;
    Muangkan, Ronnakorn
    As biometric authentication systems become ubiquitous in Southeast Asia's digital economy, sophisticated presentation attacks using deepfakes, high-resolution displays, and 3D masks pose critical security threats. This paper presents a comprehensive cross-domain liveness detection framework that addresses the generalization challenges plaguing current systems. Our approach leverages MobileNetV2-based transfer learning with a novel two-phase training strategy, achieving superior cross-domain performance while maintaining computational efficiency for mobile deployment. We introduce domain-aware augmentation techniques and evaluate our system across multiple benchmark datasets including NUAA and a locally-collected Thai demographic dataset. Experimental results demonstrate 84.35% accuracy on NUAA and 78.62% cross-domain accuracy, with significant improvements in Attack Presentation Classification Error Rate (APCER) reduction from 28.7% to 15.4% compared to baseline methods. The system successfully detects emerging attack vectors including deepfake videos and tablet-based spoofing attempts. We provide comprehensive analysis of deployment challenges in resource-constrained environments demonstrating practical applicability for Thailand's mobile banking and digital identity verification ecosystem.