In this document, I will convert the GPS way-points (with photo-link, name, latitude, longitude) into shapefile using arcpy and python modules. Find the example gpx file from this link. Use the following example code and modify according to your need. The photos are in the same folder as the gpx file.… CONTINUE READING
Category: Tutorial Page 1 of 2
Last week, I was doing some testing using the Python API interface for sentinel-1 toolbox with Snappy and the SNAP desktop to see the time taken by each of them. The workflow for processing the data is described here. The results were astonishing. Each and every steps were significantly slower, especially the co-registration process … CONTINUE READING
NASA’s Earth Observing System Data and Information System (EOSDIS) is the data distribution system facilitated with NASA’s Distributed Active Archive Centers (DAACs). Alaska Satellite Facility (ASF) is one of the DAAC providing various data sources to the public for free. We will use the ASF website (as of 28th October 2019) to download the 12.5 … CONTINUE READING
After the Geo for Good Summit 2019, all my focus has been geared toward integrating Machine Learning Models using Tensorflow with the Satellite data. One of the issues for exporting the training and testing data especially with a large number of features or huge areas for the neighborhood pixels if you are using the Fully … CONTINUE READING
Connected Pixel Count is one of the ways where the concept of the Minimum Mapping Unit (MMU) can be applied. Basically, the connected pixel count gives the image with every pixel containing the information on the number of the connected neighbors including the pixel in context. The neighbors can be 4- or 8-connected neighbors, and … CONTINUE READING
There are a couple of ways to calculate the area of the image in the Google Earth Engine. The full implementation of both method can be accessed using this link.
- Pixel Count Method
We can calculate the area of the image by counting the total number of unmasked pixels in that image. Then, multiply
In this tutorial, we are going to perform the mosaicing of two adjacent Sentinel-1 scenes using Snappy, the Python interface for SNAP.
If you haven’t, follow along this tutorial to see how you can setup the development environment for SNAP in your machine. To overview, the basic steps that we cover in this tutorial are:… CONTINUE READING
This simple UI interface is built on the Google Earth Engine. It uses the primitives as percentage layers to assemble them to derive land cover maps. Here we are using the Random Forest algorithm to generate the land cover maps.
You can use the sliders to change the threshold input for the tree node structure … CONTINUE READING
Let’s use Google Earth Engine and satellite data to visualize the time-series of the Amazon Fires 2019. In this post, I will be using FIRMS (Fire Information for Resource Management System). FIRMS disseminates Near-Real-Time active fire from the NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) satellite. This data … CONTINUE READING
The back-end on SNAP has been written in Java. But good news to Python enthusiast, they provide Python interface to Java API. It’s through their module called Snappy. In my previous tutorial, I showed you how you can install snappy in your machine and get geared up for the development work. And before we … CONTINUE READING
In this tutorial, we will learn how we can set up a development environment for Snappy. Snappy is the python interface for accessing the JAVA API of SNAP. SNAP can be used to process the Sentinel series of sensors. I prefer to have a separate environment for the Snappy so I can keep it clean … CONTINUE READING
GeoJSON is great! It can be used in the web maps, can be used as a data exchange format especially with Web Feature Service (WFS) and much more. However, sometimes the white space in the GeoJSON file can add up to the size of the file, as a result of which it becomes slower in … CONTINUE READING