├── .gitignore
├── LICENSE
├── README.md
├── lulc_py.ipynb
└── materials
├── rasters
├── s2image.tfw
├── s2image.tif
├── s2image.tif.aux.xml
├── s2image.tif.ovr
└── s2image.tif.xml
├── results
├── lulc_DT.tif
├── lulc_DT.tif.aux.xml
├── lulc_DT.tif.vat.cpg
├── lulc_DT.tif.vat.dbf
├── lulc_RF.tif
├── lulc_RF.tif.aux.xml
├── lulc_RF.tif.vat.cpg
├── lulc_RF.tif.vat.dbf
├── lulc_SVM.tif
├── lulc_SVM.tif.aux.xml
├── lulc_SVM.tif.vat.cpg
└── lulc_SVM.tif.vat.dbf
├── shapefiles
├── aoi_boundary.cpg
├── aoi_boundary.dbf
├── aoi_boundary.prj
├── aoi_boundary.sbn
├── aoi_boundary.sbx
├── aoi_boundary.shp
├── aoi_boundary.shp.xml
├── aoi_boundary.shx
├── samples.cpg
├── samples.dbf
├── samples.prj
├── samples.sbn
├── samples.sbx
├── samples.shp
├── samples.shp.xml
└── samples.shx
└── temp
├── temp_y_points.cpg
├── temp_y_points.dbf
├── temp_y_points.prj
├── temp_y_points.shp
└── temp_y_points.shx
/.gitignore:
--------------------------------------------------------------------------------
1 | # Prerequisites
2 | *.d
3 |
4 | # Compiled Object files
5 | *.slo
6 | *.lo
7 | *.o
8 | *.obj
9 |
10 | # Precompiled Headers
11 | *.gch
12 | *.pch
13 |
14 | # Compiled Dynamic libraries
15 | *.so
16 | *.dylib
17 | *.dll
18 |
19 | # Fortran module files
20 | *.mod
21 | *.smod
22 |
23 | # Compiled Static libraries
24 | *.lai
25 | *.la
26 | *.a
27 | *.lib
28 |
29 | # Executables
30 | *.exe
31 | *.out
32 | *.app
33 |
34 | # ignoring working folders
35 | arcgis/
36 | data/
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2023 Mirza Waleed
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Mastering Machine Learning based Land Use Classification with Python: A Comprehensive Guide
2 |
3 | ##
4 | Blog Link: https://medium.com/@waleedgeo/lulc-py-78cb954673d
5 |
6 | [](https://medium.com/@waleedgeo/lulc-py-78cb954673d)
7 |
8 |
9 | This repository contains the code and data used for the blog post "Mastering Machine Learning based Land Use Classification with Python: A Comprehensive Guide".
10 |
11 | In this blog post, we explore the world of machine learning based land use classification using Python. We take a deep dive into advanced techniques and algorithms for accurate land use classification. Whether you are a beginner or an experienced data scientist, this comprehensive guide will equip you with the tools and knowledge needed to master land use classification and transform the way you analyze and manage land resources.
12 |
13 | ## Contents
14 |
15 | - The materials directory contains the input rasters (rasters) and labels (shapefiles) used in this blog.
16 | - lulc_py.ipynb Jupyter Notebook is the main working file for the land use classification. It contains the code for the land use classification using machine learning algorithms.
17 |
18 | ## Requirements
19 |
20 | - Python 3.6 or later
21 | - Required Python packages: pandas, numpy, scikit-learn, matplotlib, seaborn, rasterio
22 |
23 | ## Usage
24 | 1. Clone the repository using git clone https://github.com/waleedgeo/lulc_py.git
25 | 2. Navigate to the code directory and run the lulc_py.ipynb Jupyter Notebook using any IDE of your choice. The notebook contains the code for land use classification using machine learning algorithms.
26 | 3. The results of the land use classification will be saved in the materials/results directory.
27 |
28 | ## Contributions
29 | Contributions to this repository are welcome. If you find any bugs or have suggestions for improvements, feel free to submit an issue or pull request.
30 |
31 | land-cover-classification
--------------------------------------------------------------------------------
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