Geospatial APIs For Data Science Applications In Python

Fetch geospatial data from the one map sg api to extract singapore mrt stations using python. Build a pandas dataframe by parsing json and collecting addresses, latitudes, and longitudes.

Learn to filter geospatial data by numerical attributes using greater than conditions, visualizing protected areas and computing totals like area and counts for specific regions such as Cambodia.

Learn to create a GeoJSON bounding box for an area of interest, generate a GeoJSON feature with coordinates, and use it in Google Earth Engine.

Import a user defined feature and then clip a raster elevation image to the shapefile extent, visualizing the clipped Amazonian Bolivia elevation map.

Learn how to upload external data to Google Earth Engine, including shapefiles and accompanying files, ingest assets, share access, and use them in JavaScript or Python via Colab.

Extract yearly nightlight data from an image collection, focus on Mumbai, and compare 1992 and 2012 with a split map to visualize changes in development.

Access sentinel-2 data from 2015 to present, select dry and wet season imagery, and compute the normalized difference water index with bands 3 and 8 for flood mapping.

Explain why active remote sensing uses microwave illumination and backscatter to reveal vegetation structure, as seen with alos palsar synthetic aperture radar and band penetration differences.

Obtain Landsat time series data for a point in a Thai wildlife sanctuary with the gee extract package in Python, using a 300 metre buffer, Landsat 5, start date 1985.

Explore pandas, the essential Python data science package, and its data structures: series and data frames, and learn to read data from external sources into data frames for analysis.

Convert noisy geospatial time-series data into a clean pandas DataFrame and plot with Python libraries to reveal a publication-ready time series from 2017 to 2020.

Conduct unsupervised land-cover clustering for Cambodia using Landsat 8 surface reflectance data, filter by date and cloud cover, and train with six clusters visualized via an RG color composite.

Evaluate a random forest classifier on Landsat imagery, compute confusion matrices and overall accuracy, validate with unseen data, and troubleshoot missing assets by trying other images.

Learn how spectral unmixing uses endmembers and band reflectance to classify land cover without training data. See unmixed fractions for grass, trees, soil, and water using Landsat 8.

apply supervised classification to map land cover in cambodia using geolocations, landsat data, and multiple algorithms, building robust training and testing sets to produce a land cover map.

Apply object-based image analysis in Google Earth Engine to prepare Landsat data for Ethiopia, perform normalization and band selection, generate clusters, and set the stage for random forest classification.

Distributed computing frameworks orchestrate large problems by dividing work among many nodes, enabling parallel processing, fault tolerance, scalable resource allocation, as shown by Hadoop and Apache Spark.