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This project proposes an IoT-enabled Android
application for automated road extraction and condition
monitoring using satellite and on-ground imagery. The
hardware module employs NodeMCU and ESP32-CAM to
capture real-time road surface images, which are uploaded to
Firebase for cloud-based storage and processing. A
Convolutional Neural Network (CNN), trained in Python and
converted into TensorFlow Lite (TFLite), performs feature
extraction and classifies road quality into good, moderate, or
poor categories. To further enhance decision-making, a
questionnaire interface within the app collects contextual data
such as traffic load, usage duration, and location information.
A Decision Tree algorithm integrates these metadata inputs
with CNN outputs, producing a more reliable classification.
Keywords:
Cite Article:
"Automated Road Extraction & Change Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a614-a618, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509069.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator