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<h1>🌍 <strong>MapMiner</strong> </h1>
<p>
<a href="https://colab.research.google.com/drive/1steVa5hY0SqUabvFLb0J4ypRWgSs7io9?usp=sharing" target="_blank">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/>
</a>
<img src="https://img.shields.io/badge/Python-3.x-blue.svg?style=flat-square&logo=python" alt="Python">
<img src="https://img.shields.io/badge/Xarray-0.18+-orange.svg?style=flat-square&logo=xarray" alt="Xarray">
<img src="https://img.shields.io/badge/Dask-Powered-yellow.svg?style=flat-square&logo=dask" alt="Dask">
<img src="https://img.shields.io/badge/Numba-Accelerated-green.svg?style=flat-square&logo=numba" alt="Numba">
<img src="https://img.shields.io/badge/Selenium-Automated-informational.svg?style=flat-square&logo=selenium" alt="Selenium">
</p>
<p><strong>MapMiner</strong> is a geospatial tool designed to efficiently download and process geospatial data and metadata from various sources. It leverages powerful Python libraries like <strong>Selenium</strong>, <strong>Dask</strong>, <strong>Numba</strong>, and <strong>Xarray</strong> to provide high-performance data retrieval and processing capabilities for geospatial analysis and visualization.</p><br>
<h2>🚀 <strong>Key Features</strong></h2>
<ul>
<li><strong>🌐 Selenium:</strong> Automated web interactions for metadata extraction.</li>
<li><strong>⚙️ Dask:</strong> Distributed computing to manage large datasets.</li>
<li><strong>🚀 Numba:</strong> JIT compilation for accelerating numerical computations.</li>
<li><strong>📊 Xarray:</strong> Multi-dimensional array data handling for seamless integration.</li>
</ul><br><h2>📚 <strong>Supported Datasets</strong></h2>
<p>MapMiner supports a variety of geospatial datasets across multiple categories:</p>
<div>
| Category | Datasets |
|-------------------------------------|---------------------------------------------------------------------------|
| 🌍 **Satellite** | `Sentinel-2`, `Sentinel-1`, `MODIS`, `Landsat` |
| 🚁 **Aerial** | `NAIP` |
| 🗺️ **Basemap** | `Google`, `ESRI` |
| 📍 **Vectors** | `Google Building Footprint`, `OSM` |
| 🏔️ **DEM (Digital Elevation Model)** | `Copernicus DEM 30m`, `ALOS DEM` |
| 🌍 **LULC (Land Use Land Cover)** | `ESRI LULC` |
| 🌾 **Crop Layer** | `CDL Crop Mask` |
| 🕒 **Real-Time** | `Google Maps Real-Time Traffic` |
<h2>🛠 <strong>Installation</strong></h2>
<p>Ensure you have the necessary dependencies installed:</p>
<pre><code class="highlight">pip3 install mapminer</code></pre>
<h2>📝 <strong>Usage</strong></h2>
<p>MapMiner provides multiple classes to fetch and process different types of geospatial data:</p>
<h3><strong>1️⃣ GoogleBaseMapMiner</strong></h3>
<pre><code>from mapminer.miner import GoogleBaseMapMiner
miner = GoogleBaseMapMiner()
ds = miner.fetch(lat=40.748817, lon=-73.985428, radius=500)</code></pre>
<h3><strong>2️⃣ CDLMiner</strong></h3>
<pre><code>from mapminer.miner import CDLMiner
miner = CDLMiner()
ds = miner.fetch(lon=-95.665, lat=39.8283, radius=10000, daterange="2024-01-01/2024-01-10")</code></pre>
<h3><strong>3️⃣ GoogleBuildingMiner</strong></h3>
<pre><code>from mapminer.miner import GoogleBuildingMiner
miner = GoogleBuildingMiner()
ds = miner.fetch(lat=34.052235, lon=-118.243683, radius=1000)</code></pre>
<h2>🖼 <strong>Visualizing the Data</strong></h2>
<p>You can easily visualize the data fetched using <code class="highlight">hvplot</code>:</p>
<pre><code>import hvplot.xarray
ds.hvplot.image(title=f"Captured on {ds.attrs['metadata']['date']['value']}")</code></pre>
<h2>📦 <strong>Dependencies</strong></h2>
<p>MapMiner relies on several Python libraries:</p>
<ul>
<li><strong class="important">Selenium:</strong> For automated browser control.</li>
<li><strong class="important">Dask:</strong> For distributed computing and handling large data.</li>
<li><strong class="important">Numba:</strong> For accelerating numerical operations.</li>
<li><strong class="important">Xarray:</strong> For handling multi-dimensional array data.</li>
<li><strong class="important">EasyOCR:</strong> For extracting text from images.</li>
<li><strong class="important">HvPlot:</strong> For visualizing xarray data.</li>
</ul>
<h2>🛠 <strong>Contributing</strong></h2>
<p>Contributions are welcome! Fork the repository and submit pull requests. Include tests for any new features or bug fixes.</p>
</body>
</html>
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"description": "<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n <meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n</head>\n<body>\n <h1>\ud83c\udf0d <strong>MapMiner</strong> </h1>\n <p>\n <a href=\"https://colab.research.google.com/drive/1steVa5hY0SqUabvFLb0J4ypRWgSs7io9?usp=sharing\" target=\"_blank\">\n <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/>\n</a>\n <img src=\"https://img.shields.io/badge/Python-3.x-blue.svg?style=flat-square&logo=python\" alt=\"Python\">\n <img src=\"https://img.shields.io/badge/Xarray-0.18+-orange.svg?style=flat-square&logo=xarray\" alt=\"Xarray\">\n <img src=\"https://img.shields.io/badge/Dask-Powered-yellow.svg?style=flat-square&logo=dask\" alt=\"Dask\">\n <img src=\"https://img.shields.io/badge/Numba-Accelerated-green.svg?style=flat-square&logo=numba\" alt=\"Numba\">\n <img src=\"https://img.shields.io/badge/Selenium-Automated-informational.svg?style=flat-square&logo=selenium\" alt=\"Selenium\">\n </p>\n <p><strong>MapMiner</strong> is a geospatial tool designed to efficiently download and process geospatial data and metadata from various sources. It leverages powerful Python libraries like <strong>Selenium</strong>, <strong>Dask</strong>, <strong>Numba</strong>, and <strong>Xarray</strong> to provide high-performance data retrieval and processing capabilities for geospatial analysis and visualization.</p><br>\n <h2>\ud83d\ude80 <strong>Key Features</strong></h2>\n <ul>\n <li><strong>\ud83c\udf10 Selenium:</strong> Automated web interactions for metadata extraction.</li>\n <li><strong>\u2699\ufe0f Dask:</strong> Distributed computing to manage large datasets.</li>\n <li><strong>\ud83d\ude80 Numba:</strong> JIT compilation for accelerating numerical computations.</li>\n <li><strong>\ud83d\udcca Xarray:</strong> Multi-dimensional array data handling for seamless integration.</li>\n </ul><br><h2>\ud83d\udcda <strong>Supported Datasets</strong></h2>\n<p>MapMiner supports a variety of geospatial datasets across multiple categories:</p>\n<div>\n\n\n| Category | Datasets |\n|-------------------------------------|---------------------------------------------------------------------------|\n| \ud83c\udf0d **Satellite** | `Sentinel-2`, `Sentinel-1`, `MODIS`, `Landsat` |\n| \ud83d\ude81 **Aerial** | `NAIP` |\n| \ud83d\uddfa\ufe0f **Basemap** | `Google`, `ESRI` |\n| \ud83d\udccd **Vectors** | `Google Building Footprint`, `OSM` |\n| \ud83c\udfd4\ufe0f **DEM (Digital Elevation Model)** | `Copernicus DEM 30m`, `ALOS DEM` |\n| \ud83c\udf0d **LULC (Land Use Land Cover)** | `ESRI LULC` |\n| \ud83c\udf3e **Crop Layer** | `CDL Crop Mask` |\n| \ud83d\udd52 **Real-Time** | `Google Maps Real-Time Traffic` |\n\n\n\n<h2>\ud83d\udee0 <strong>Installation</strong></h2>\n<p>Ensure you have the necessary dependencies installed:</p>\n<pre><code class=\"highlight\">pip3 install mapminer</code></pre>\n <h2>\ud83d\udcdd <strong>Usage</strong></h2>\n <p>MapMiner provides multiple classes to fetch and process different types of geospatial data:</p>\n <h3><strong>1\ufe0f\u20e3 GoogleBaseMapMiner</strong></h3>\n <pre><code>from mapminer.miner import GoogleBaseMapMiner\nminer = GoogleBaseMapMiner()\nds = miner.fetch(lat=40.748817, lon=-73.985428, radius=500)</code></pre>\n <h3><strong>2\ufe0f\u20e3 CDLMiner</strong></h3>\n <pre><code>from mapminer.miner import CDLMiner\nminer = CDLMiner()\nds = miner.fetch(lon=-95.665, lat=39.8283, radius=10000, daterange=\"2024-01-01/2024-01-10\")</code></pre>\n <h3><strong>3\ufe0f\u20e3 GoogleBuildingMiner</strong></h3>\n <pre><code>from mapminer.miner import GoogleBuildingMiner\nminer = GoogleBuildingMiner()\nds = miner.fetch(lat=34.052235, lon=-118.243683, radius=1000)</code></pre>\n <h2>\ud83d\uddbc <strong>Visualizing the Data</strong></h2>\n <p>You can easily visualize the data fetched using <code class=\"highlight\">hvplot</code>:</p>\n <pre><code>import hvplot.xarray\nds.hvplot.image(title=f\"Captured on {ds.attrs['metadata']['date']['value']}\")</code></pre>\n <h2>\ud83d\udce6 <strong>Dependencies</strong></h2>\n <p>MapMiner relies on several Python libraries:</p>\n <ul>\n <li><strong class=\"important\">Selenium:</strong> For automated browser control.</li>\n <li><strong class=\"important\">Dask:</strong> For distributed computing and handling large data.</li>\n <li><strong class=\"important\">Numba:</strong> For accelerating numerical operations.</li>\n <li><strong class=\"important\">Xarray:</strong> For handling multi-dimensional array data.</li>\n <li><strong class=\"important\">EasyOCR:</strong> For extracting text from images.</li>\n <li><strong class=\"important\">HvPlot:</strong> For visualizing xarray data.</li>\n </ul>\n <h2>\ud83d\udee0 <strong>Contributing</strong></h2>\n <p>Contributions are welcome! Fork the repository and submit pull requests. Include tests for any new features or bug fixes.</p>\n</body>\n</html>\n",
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