Integration of GIS and machine learning analytics into Streamlit application
Noyan Aendikov, Aeila Azayeva · Procedia Computer Science · 2024
This paper introduces an implementation of different GIS tools into Streamlit application: FCNN Terrain Classification, Earth Engine Dataset Parsing, and GIS Timelapse Animations. This toolkit is integrated with terrain multi-classification models using Fully Convolutional Neural Networks (FCNNs) for imagery data into Streamlit microservices. The proposed methodology involves labeled and unlabeled data collection from ESA WorldCover and Sentinel-2 MSI on the Google Earth Engine, compressing datasets into TFRecords format with 9 diverse terrain types, and handling Google Cloud training computations. The experimental results demonstrate the effectiveness of the CNN-based approach, achieving a tolerable from 60% up to 80% accuracy of the model and robust classification performance. The simplicity and efficiency of the proposed method make it suitable for real-world tasks requiring reliable and fast GIS analytics.