International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Deep Geo Detect: A Hybrid AI Framework for Intelligent Geospatial Change Monitoring

Author(s) Mr. Basavaraju Pavan Kumar, Mr. Badireddy Praneeth ., Mr. Satya Sai ., Mr. Porla Mukesh Yadav, Battula Balnarsaiah, Sunil Tekale
Country India
Abstract Geospatial Artificial Intelligence (GeoAI) is an important component in the analysis of multi temporal satellite imagery in order to comprehend spatial changes in dynamic environments. In the present paper, Deep Geo Detect is introduced as a hybrid deep learning model that is aimed at automated geographic change detection by combining Convolutional Neural Networks (CNNs) and a Random Forest (RF) classifier. The approach suggested is a patch based learning scheme on multi spectral satellite image where the CNN element is used to learn hierarchical spatial spectral representations using bi-temporal image pairs. A Random Forest classifier then uses these deep representations to improve the stability of decisions and deal with the interactions between features. To overcome the difficulties that are normally related to change detection, such as class imbalance and spatial heterogeneity, a hierarchical patch labeling mechanism and data balancing plan are included in the training pipeline. The framework can be used to capture both localized and large scale environmental changes, thus making it scalable to various geographic settings.The proposed architecture can be used to develop a scalable and interpretable remote sensing based change analysis system by integrating deep feature learning with ensemble based classification.
Keywords Geospatial Artificial Intelligence (Geo AI), Change Detection, Convolutional Neural Networks, Random Forest, Hybrid Learning Framework, Remote Sensing, MultiTemporal Satellite Imagery, Patch-Based Classification.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-29
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.71860
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals