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Preprints

Filtering by Subject: Computer Engineering

A Community-Oriented Flood Map Visualization and Evaluation Framework

Moiyyad Sufi, Carlos Erazo, Bong-Chul Seo, et al.

Published: 2026-10-15
Subjects: Civil and Environmental Engineering, Computer Engineering, Computer Sciences, Engineering, Hydraulic Engineering, Hydrology, Software Engineering

Flood inundation maps are essential for preparedness, response, and long‐term risk mitigation, yet many existing visualization platforms remain limited by rigid data structures and region‐specific workflows. This study introduces FIMApp, a client‐side, web‐based framework and platform for integrating, visualizing, and comprehensively evaluating flood map products using standardized, portable data [...]

GeONet: Geometry-Encoded Operator Network for Predicting Flow in Heterogeneous Porous Media

Md Lal Mamud, Maruti Mudunuru, Yucheng Fu, et al.

Published: 2026-10-15
Subjects: Civil and Environmental Engineering, Computer Engineering, Earth Sciences, Engineering, Environmental Sciences, Mathematics, Physical Sciences and Mathematics

Rapid prediction of pressure-driven flow in microfluidic porous-media analogs is important for experimental design, tracer-test interpretation, and real-time digital-twin development for subsurface energy and critical mineral science applications. High-fidelity numerical simulators resolve geometry-controlled flow in heterogeneous pore networks, but their computational cost limits repeated [...]

ConLoRA-Sat: Parameter-Efficient Synthetic Satellite Imagery Generation for Data-Scarce Humanitarian Contexts

Seyed Amir Muhammad Qureshi, Carlos Granell, Sven Casrteleyn

Published: 2026-10-14
Subjects: Computer Engineering, Geographic Information Sciences, Remote Sensing, Spatial Science

Data scarcity constrains Geospatial Artificial Intelligence (GeoAI) in regions such as the Horn of Africa. One example is United Nations peacekeeping training, which needs fictitious yet geographically plausible terrain rather than identifiable real locations. This article presents ConLoRA-Sat, a framework that generates satellite imagery with controllable land cover, together with a segmentation [...]

Coupled Physics-Based and Machine Learning Streamflow Modeling with High-Resolution Flood Inundation and Impact Assessment for Hurricane Florence (2018): A Case Study of the Neuse River at Goldsboro, North Carolina

H M Ashaduzzaman Asad

Published: 2026-09-18
Subjects: Computer Engineering, Earth Sciences, Education, Engineering, Environmental Monitoring, Planetary Hydrology, Probability

Hurricane Florence made landfall in North Carolina in September 2018 and produced record or near-record river stages across the Neuse River basin, including at United States Geological Survey (USGS) gauge 02089000 near Goldsboro. This study presents an end-to-end hydrological and floodmapping workflow that couples a semi-distributed, physics-based rainfall-runoff model (Physics V3.5) and a [...]

Deep Learning-Based Meteorological Data Downscaling: A Comparative Study with Physics Informed CNN and a Component-Level Ablation Analysis

Johans Utama

Published: 2026-08-29
Subjects: Computer Engineering, Earth Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Meteorological statistical downscaling is a critical technique for deriving high-resolution climate information from coarse global reanalysis products. This experiment investigates the application of five representative deep learning architectures for downscaling ERA5 reanalysis 2m temperature fields from 1° to 0.25° spatial resolution (4x upscaling factor) over the China region, spanning the [...]

Field-scale sugarcane mapping in Thailand by fusing annual satellite embeddings with a global field-boundary model, cross-checked against mill weighbridge records

Watcharaphon Thodsrakza

Published: 2026-08-15
Subjects: Computer and Systems Architecture, Computer Engineering, Engineering

Sugarcane in Thailand is grown almost entirely by smallholders who deliver to mills under seasonal contracts, so the quantities that matter to planning are properties of individual fields rather than of pixels. Published Thai cane maps are per-pixel classifications, and global cane products perform markedly worse in Thailand than elsewhere because cane is confused with cassava. Whether the global [...]

Pixel-Level Urban Housing-Price Mapping Based on AlphaEarth Foundations: Evidence from 36 Chinese Cities

Shuyang Hou, Haoyue Jiao, Ziqi Liu, et al.

Published: 2026-07-04
Subjects: Computer Engineering, Computer Sciences, Geographic Information Sciences, Human Geography, Remote Sensing

This study develops and validates a multi-source modelling framework for continuous, pixel-level urban housing-price mapping using surface embeddings from AlphaEarth Foundations (AEF). Pixel-level labels calibrated against multi-source market data are constructed for 288 city–year samples across 36 Chinese cities (2017–2024), and AEF’s 64-dimensional, 10 m annual surface embeddings are [...]

PharmGuard: A blockchain and LLM-integrated framework with provenance-aware anomaly scoring for securing pharmaceutical supply chains

Tahsinul Haque Dhrubo, Ayesha Siddika, Zarin Saima

Published: 2026-04-08
Subjects: Computer Engineering

Counterfeit and substandard medicines remain a major global health threat, underscoring the need for end-to-end traceability and proactive monitoring across pharmaceutical supply chains. This paper presents PharmGuard, a framework that combines a permissioned blockchain ledger (Hyperledger Fabric) for tamper-evident provenance with a large-language-model analytics layer for detecting anomalous [...]

Survey Protocol Cards for Crop Maps

Akram Zaytar, Girmaw Abebe Tadesse, Caleb Robinson, et al.

Published: 2026-04-04
Subjects: Agriculture, Computer Engineering

Crop type maps underpin food security decisions yet their accuracy depends on label quality, which in turn depends on survey design choices made under tight budgets. Survey planners must allocate limited resources across GPS devices, stratification strategies, sample size, worker training, and verification protocols, but lack quantitative guidance on which investments yield quality crop maps. We [...]

DHAFGan: A Dense Hybrid Attention Fusion Generative Adversarial Network for Infrared and Visible Image Fusion

Qiong Hong, Zhonghua Xu, Dongli Qin, et al.

Published: 2026-03-21
Subjects: Computer Engineering

Aiming at the problems existing in the current infrared and visible light image fusion algorithms, such as insufficient perception of typical features, poor visual representation of the fusion results, and insufficient utilization of important secondary information, this paper proposes an infrared and visible light image fusion algorithm based on shallow-deep feature extraction and dual-channel [...]

HydroVerse Education: An AI-Assisted Education Framework for Immersive Learning and Training Environments

Ali Rahmani, Yusuf Sermet, Ibrahim Demir

Published: 2025-12-22
Subjects: Adult and Continuing Education, Computer Engineering, Educational Methods, Engineering Education, Environmental Education, Environmental Engineering, Environmental Sciences

Hydroinformatics education has traditionally been constrained by pedagogical approaches that fail to adequately convey the complexity of water systems. Virtual Reality (VR) presents a transformative solution by enabling interactive and experiential learning. This study introduces HydroVerse Education, an immersive virtual classroom environment designed to modernize hydroinformatics education by [...]

HydroVerse VR Equipment Hub: An Integrated Virtual Reality Framework for Training and Workforce Development in Environmental Monitoring

Ali Rahmani, Yusuf Sermet, Ibrahim Demir

Published: 2025-10-21
Subjects: Adult and Continuing Education, Computer Engineering, Educational Methods, Engineering Education, Environmental Education, Environmental Engineering, Environmental Monitoring

Environmental monitoring is critical for managing ecosystems and addressing global challenges such as climate change and pollution. However, training professionals in this field is often hampered by remote locations, hazardous field conditions, and limited access to specialized equipment. In response, this paper presents the HydroVerse VR Equipment Hub, an immersive virtual reality framework for [...]

Smart Urban Design with Physics-Informed Neural Networks: Quantifying Temperature Reductions from Green Infrastructure Using Satellite Thermal Data

Dung Thi Vu, Rafeeque Ahmed Nizamani, Abdul Ghafoor Nizamani

Published: 2025-10-19
Subjects: Civil and Environmental Engineering, Computer Engineering, Engineering, Environmental Engineering

Urban Heat Islands (UHIs), characterised by elevated temperatures in densely built environments, pose critical challenges to urban sustainability, public health, and energy resilience. Mitigating UHIs requires precise quantification of the cooling effects of green infrastructure; however, existing models often fail to integrate high-resolution geospatial data with physical laws. This study [...]

Vector Graphics-Based Geospatial Contour Maps: A Web-Native Interactive Approach for Modern Geospatial Data Science Applications

Amandip Sangha

Published: 2025-10-13
Subjects: Computer Engineering, Computer Sciences, Earth Sciences, Engineering, Environmental Sciences, Graphics and Human Computer Interfaces

Visualizing scalar fields (e.g., temperature, precipitation etc.) on the web is often done via pre‑rendered raster tiles. While simple to serve and fast to access, rasters limit interactivity (feature picking, dynamic styling) and typically require heavy pre‑generation pipelines. Raster tiles remain the dominant method for web-based scalar field visualization, but they are storage-heavy and [...]

Unsupervised Concept Discovery for Deep Weather Forecast Models with High-Resolution Radar Data

Soyeon Kim, Junho Choi, Subeen Lee, et al.

Published: 2025-05-01
Subjects: Computer Engineering

The global climate crisis is creating increasingly complex rainfall patterns, leading to a rising demand for data-driven artificial intelligence (AI) in short-term weather forecasting. However, the black-box nature of AI models act as a critical obstacle against their integration into existing forecasting operations. This study addresses this issue by implementing an explainable AI framework that [...]

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