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.

Use the Foursquare API for geolocation to find venues; register a developer app to obtain a client id and secret, then access venue tips.

Retrieve venues near specific lat-long coordinates using an API, parse results, and normalize them into a dataframe to reveal cafes and relaxation venues around the London Eye.

Learn to fetch venues near Mumbai neighborhoods using geospatial APIs in Python, with lat-long, a radius 5000, and a dataframe of venues by category.

Sign up for Google Earth Engine with a Gmail account to access the code editor, api, and geospatial datasets. Learn basic programming techniques to visualize and work with these datasets.

Explore the Google Earth Engine dataset catalog with Landsat datasets, 32-day composites, enhanced vegetation index, and forest change data, opened in workspace to assess greenery values.

Explore geemap in Google Colab to visualize geospatial data with Google Earth Engine, authenticate, and switch between diverse basemaps for interactive mapping.

Identify the biggest country by comparing shape area for Brazil, Peru, Colombia, and Chile, then visualize with a descending bar plot.

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.

Visualize a time series of the normalized difference vegetation index (NDB) for a Cambodian protected area using MODIS data from 2017–2019, with scaling and mean reduction in Python.

Explore how optical passive remote sensing collects multispectral data across the electromagnetic spectrum, with vegetation reflectance peaking in the near infrared, enabling mapping of forests and land use.

Explore multispectral data from Sentinel-2 to perform flood mapping in the Tonle Sap region of Cambodia, capturing wet and dry season dynamics, cloud challenges, and access via Copernicus datasets.

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.

visualize sentinel-1 data by converting to an image, building an rgb composite from polarizations, applying minimum values, and inspecting backscatter patterns for biomass modeling and classification in a protected area.

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.

Plot Singapore's MRT stations on a folium map using latitude and longitude. Color-code by zone and add interactive pop-up labels to explore geospatial visualization.

Learn unsupervised learning for imagery analysis in remote sensing by clustering data from spectral bands into land use patterns without labels.

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.

Perform supervised classification by sampling 5000 training points from MODIS land cover data integrated with Landsat, then train a random forest classifier and classify the Landsat image.

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.

Understand how confusion matrices measure accuracy in binary and multiclass problems, and apply metrics such as precision, recall (sensitivity), true positives/negatives, false positives/negatives, and Cohen's kappa.

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.

Visualize classification results by engineering vegetation indices and band combinations from fused Sentinel-1 and Sentinel-2 data, and compare training versus testing accuracy across models.

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.

Explore numpy, the numerical Python package, and learn how numpy arrays enable multi-dimensional data operations, from basic arithmetic to solving linear systems, with standard import conventions like np and pd.

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.