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International Journal of Information Technology and Computer Science Applications (IJITCSA) Sekretariat Jejaring Penelitian dan Pengabdian Masyarakat (JPPM) : Ranau Estate Blok D.3, Kel. Panggungjati, Kp. Pantogan Kec. Taktakan - Kota Serang, Provinsi Banten, e-mail : jitcsa@jejaringppm.org web : www.jejaringppm.org
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International Journal of Information Technology and Computer Science Applications (IJITCSA)
Published by Jejaring Penelitian dan Pengabdian Masyarakat
ISSN : 29643139     EISSN : 29855330     DOI : https://doi.org/10.58776/ijitcsa.v1i2
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering
he Journal of Information Technology and Computer Science Applications (JITCSA) is an information technology and computer science publication. Applications from both fields for solving real cases are also welcome. JITCSA accepts research articles, systematic reviews, literature studies, and other relevant ones. Several fields of science that are the focus of JITCSA include information technology and the like, computer science fields, including artificial intelligence, data science, data mining, machine learning, deep learning, and the like. IJITCSA is published three times a year, in January, May, and September. The first issue in January 2023 had eight articles. Focus and Scope International Journal of Information Technology and Computer Science Applications includes scholarly writings on scientific research or review, pure research, and applied research in the field of computer science, information systems, and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. Information systems System Software Artificial Intelligence Computer Architecture Distributed Systems System & Software Engineering Genomics & Bioinformatics Internet and Web AI & Expert systems Software Process and Life Cycle Database Systems Software Testing & Quality assurance Bioinformatics Information Technology Implementation Computing Languages & Algorithms E-commerce & M-Commerce Computer Networks & Communications Computing Systems Control Systems & Engineering Systems Engineering System Security Digital Forensics Data Mining & Machine Learning Data Modeling
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 73 Documents
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Modality-Resilient Multimodal Earth-Observation Foundation Models under Missing and Corrupted Sensors Anna Kalaitzis Pollan
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.259

Abstract

Multimodal Earth-observation (EO) foundation models increasingly rely on the joint use of optical imagery, synthetic aperture radar (SAR), elevation, and auxiliary geospatial signals to support land-cover mapping, biomass estimation, damage assessment, and change detection. Yet the empirical regime in which such models are usually trained and reported remains overly optimistic: modalities are commonly assumed to be synchronised, complete, and clean. This assumption is not defensible for real deployments. Optical observations are routinely obscured by clouds, SAR coherence and interferometric products can be unavailable or decorrelated, spatial alignment across sources is imperfect, and large geospatial archives inevitably contain broken tiles, missing channels, or temporally inconsistent acquisitions. Starting from the released M3LEO dataset and framework, which already expose multimodal EO learning at continental scale, this paper reconstructs the underlying research direction toward a more technically urgent problem: robust multimodal foundation learning under missing or corrupted modalities. We formalise a modality-resilient framework, RAMEO, that combines modality-specific token encoders, reliability-aware gated fusion, masked cross-modal reconstruction, corruption-aware consistency learning, and calibrated uncertainty estimation. We also define a rigorous evaluation protocol over geographically disjoint splits, structured missingness patterns, and modality-specific corruptions. This paper done a dataset-and-benchmark paper and reconstruction distinguishes carefully between source-anchored evidence reported for M3LEO and new robustness analyses that are specified as executable experiments. The result is therefore technically coherent, and reproducible, while remaining honest about what has and has not yet been empirically established.
Geospatial Vision-Language Models for Spatial Reasoning and Temporal Change Understanding: A Task-Centered Benchmarking Framework and Evidence Synthesis Discussion Abeer Hasshen Abdullah
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.262

Abstract

Geospatial vision-language models (VLMs) are increasingly expected to do more than assign scene labels or generate generic captions. In realistic Earth-observation and urban intelligence settings, useful multimodal systems must support fine-grained spatial reasoning, cross-view interpretation, grounded localization, and explicit understanding of change across time. Yet the current literature remains fragmented across remote sensing visual question answering, visual grounding, urban multi-view reasoning, and bi-temporal change captioning. As a result, claims about progress are often task-local, benchmark-specific, and difficult to compare. This paper reconstructs the field around a more defensible technical center: geospatial multimodal intelligence as the joint problem of spatial reasoning and temporal change understanding. Rather than presenting unverifiable new benchmark runs, we develop a rigorous review paper anchored in public benchmark evidence and a reference architecture for reproducible future work. We first formalize a task-centered problem definition that unifies image-level, region-level, cross-view, and bi-temporal reasoning. We then propose a reference GST-VLM architecture consisting of spatial encoding, temporal difference modeling, multimodal fusion, task-specific decoding, and reliability estimation. Next, we synthesize publicly reported evidence from representative datasets and benchmarks including RSVQA, EarthVQA, VRSBench, GeoChat, LEVIR-CD, LEVIR-CC, SECOND-CC, CHOICE, GEOBench-VLM, CityBench, and UrBench. The synthesis shows that recent models are improving rapidly but remain far from robust geospatial reasoning systems: on GEOBench-VLM, the best public model reported only 41.7% multiple-choice accuracy; on UrBench, even GPT-4o still trails human performance by an average 17.4 percentage points; and while specialized systems such as GeoReasoner, GeoChat, GeoLLaVA, and MModalCC outperform generic baselines on targeted tasks, their gains remain strongly benchmark-dependent. Based on this evidence, we identify the principal bottlenecks as benchmark fragmentation, weak temporal grounding, inadequate calibration, scarce cross-region validation, limited deployment reporting, and insufficient integration of geometry with language-conditioned reasoning. The paper concludes with a concrete research agenda for trustworthy geospatial VLMs that is centered on multi-temporal supervision, interactive change analysis, uncertainty-aware outputs, and evaluation protocols that measure not only accuracy but also transfer, calibration, and operational feasibility.
Literature Review of Hybrid Neural–Physics Flood-Relevant Streamflow Forecasting with Cross-Event and Cross-Basin Generalization Yanan Elliot Moko
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 2 (2026): May - August 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i2.263

Abstract

Reliable flood-relevant streamflow forecasting remains difficult at operational scale because physically based forecasting systems provide national coverage but cannot be directly verified at the overwhelming majority of ungauged river reaches. This study reconstructs and strengthens a hybrid neural–physics framework in which the National Water Model (NWM) supplies the physically constrained baseline forecast and an attribute-conditioned deep neural network learns forecast-error magnitude from gauged basins, then transfers that knowledge to ungauged basins. The emphasis is not on replacing process-based hydrology with a black-box predictor, but on improving forecast reliability under cross-event and cross-basin generalization. The empirical setting consists of 979 archived daily long-range forecasts, 30 forecast lead days, 16-member NWM ensembles, and 389 quality-controlled U.S. Geological Survey gauging stations across Alabama and Georgia, complemented by static watershed descriptors and gridded soil-moisture diagnostics. Exploratory analysis shows that forecast skill is strongly conditioned by basin properties: larger and forested watersheds are predicted more accurately than smaller and urban watersheds, while urban basins show severe positive bias, weaker anomaly correlation, and degraded hydrograph timing. These diagnostics motivate an error-learning postprocessor that maps NWM forecast magnitude and basin attributes to a non-negative error estimate and converts the deterministic physics forecast into a corrected predictive interval. Under a temporally disjoint evaluation intended to test cross-event transfer, observation coverage increases from 0.21±0.01 for the raw NWM ensemble range to 0.82±0.03 for the hybrid model; a repeated spatial holdout yields comparable performance (0.82±0.01). The gains are largest where the underlying physics model is weakest, especially in developed watersheds. The main limitation is that improved reliability is achieved through wider predictive intervals rather than sharper hydrographs. The study therefore supports a defensible conclusion of hybrid neural–physics postprocessing can materially improve flood-relevant streamflow reliability at ungauged basins, but interval sharpness and external cross-region validation remain open research needs.

Page 8 of 8 | Total Record : 73

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