vannolimarco/classification-for-land-use-images-via-cnn
Performing a classification of the land-use image provided by a Remote sensing process using a Convolution-Neural-Network trained through pre-trained neural network VGG16 (transfer learning) 🛰️🛰️🛰️
This project helps urban planners, environmental analysts, and geographers automatically identify different land uses from satellite images. It takes raw satellite imagery and classifies each image into one of 21 categories, such as agricultural land, forests, or residential areas. The output is a clear classification of the land use, helping professionals quickly understand and map large geographical regions.
No commits in the last 6 months.
Use this if you need to automatically categorize satellite or aerial images to understand land use patterns across urban and rural areas.
Not ideal if you need to classify images for purposes other than land use (e.g., medical imaging, facial recognition) or if your images contain fewer than 21 distinct land-use categories.
Stars
14
Forks
2
Language
Python
License
—
Category
Last pushed
Mar 10, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/vannolimarco/classification-for-land-use-images-via-cnn"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
maja601/EuroCrops
The official repository for the EuroCrops dataset.
dida-do/eurocropsml
EuroCropsML is a ready-to-use benchmark dataset for few-shot crop type classification using...
langnico/global-canopy-height-model
This repository contains the code used in the paper: A high-resolution canopy height model of...
raoofnaushad/Land-Cover-Classification-using-Sentinel-2-Dataset
Application of deep learning on Satellite Imagery of Sentinel-2 satellite that move around the...
clejae/europe_land_iacs_prep
Preprocessing and harmonization scripts for IACS/GSA data.