At AgriTech, we are very interested in how water flows through a maize farm field. This knowledge will help us improve our research on new agricultural products being tested on farms.
LiDAR (light detection and ranging) is a popular remote sensing mechanism used for measuring the exact distance of an object on the earth's surface. Since the introduction of GPS technology, it has become a widely used method for calculating accurate geospatial measurements. These geospatial data are used for different analysis purposes.
The purpose of this project is to build models of water flow and predict maize harvest if we better understand how water flows through a field, and which parts are likely to be flooded or too dry.
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Main Tasks
- Enable Elevation Data Fetching
- Enable Data Loading from saved tif and las/laz files
- Enable Terrian Visualization using retrieved or loaded LiDAR cloud points
- Enable Cloud Point Standardizing/Sub-Sampling
- Enable data augmentation to retrieved geopandas data-frame
- Composing a QuickStart Guide Notebook
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Additional Tasks
- Enable Diagrammatic way of comparing original terrain and subsampled terrain
- Enable Soil-Data Fetching
- Enable Climate-Data Fetching
- Enable interaction with Sentinel public API
- Enable users to download satellite imagery using Sentinels API
Some of the python packages required to do the project are listed here and for more check the requiremnt.txt file:
pip install PDAL
pip install geopandas
pip install rasterio
pip install laspy
Skills:
- Working with satellite imagery as well as geographical data files
- Exposure to building API that interacts with satellite imagery Code packaging and modularity
- Building data pipelines and orchestrations workflows
Knowledge:
- Satellite and geographical Image processing
- Functional and Modular Coding
- API access to Big Data
- https://www.earthdatascience.org/courses/use-data-open-source-python/data-stories/what-is-lidar-data/explore-lidar-point-clouds-plasio/
- https://pdal.io/tutorial/iowa-entwine.html
- https://paulojraposo.github.io/pages/PDAL_tutorial.html
- https://www.earthdatascience.org/courses/use-data-open-source-python/intro-vector-data-python/spatial-data-vector-shapefiles/intro-to-coordinate-reference-systems-python/
- https://towardsdatascience.com/how-to-automate-lidar-point-cloud-processing-with-python-a027454a536c
- https://towardsdatascience.com/farm-design-with-qgis-3fb3ea75bc91