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Embark on an exciting journey as we reveal the wizardry behind transforming your QGIS projects into captivating web maps. In just a few clicks, you can breathe life into your static visualizations, creating an interactive online experience that's both powerful and effortless. Don't miss out on this game-changing tutorial – the link to the video awaits you at the end of this blog post! Discover the incredible capabilities of the OpenGIS Plugin as it effortlessly bridges the gap between your QGIS desktop and the online realm. From intricate heatmaps to sophisticated choropleths, every nuance of your original visualizations is preserved. This is your chance to elevate your maps to new heights, making them not just informative but engaging. Click the video link to witness the transformation firsthand. Flexibility meets convenience with the OpenGIS Plugin. Once your web map masterpiece is ready, the choice of deployment is yours. Whether it's the familiar GitHub or other hosting...

Here is a short video (under one minute) tutorial about how to perform and calculate Origin - Destination matrix or Distance Matrix between two points layers in QGIS. 

How to generate flat terrain and no topographic undulation in Satellite Imagery. Topographic correction is very helpful for Landcover/landuse classification in steep, hilly or mountainous terrain. Topographic correction guarantee more spectral consistent values for similar landcover in the sun side slopes and shadowed slopes. In the end, more accurate landcover classification can be obtained.

How to perform minerals identification and classification of rocks and soil using spectral angle mapper classifier and ENVI software. This demo applied using ASTER imagery, however it is can be extended to Landsat or Sentinel Imagery. Works better in open terrain such as arid or desert region with minimum to none of vegetation cover. Surface reflectance data (atmospherically corrected) is mandatory for the best result. Field inspection also mandatory to assess the classification accuracy because SAM is very sensitive to different parameter settings.

Color composites generation workflow which emphasizes to mineral (clay, iron oxide, and ferrous minerals) distribution over wide area. This workflow can be applied to Landsat-8 or Landsat-9 imagery. Similar workflow also can be done for Sentinel-2 MSI imagery. Workflow can be applied to Level-1 data, and the most optimal is applied to the surface reflectance level data. Key interpretation about color and mineralogy association: 1. Reddish color = ferrous minerals 2. Greenish color = clay minerals 3. Bluish color = iron oxide minerals

Hi, this post is about four solutions about how to perform offline basemap creation for mobile mapping in Android or Apple IOS devices. For this first part. I will demonstrate about GeoPDF format that is widely supported in most GIS Software. I will demonstrate how to write GeoPDF in ArcGIS Desktop (ArcGIS Pro also has this functionality) and QGIS, followed by how to load the GeoPDF Map in a supported Android/IOS App. Unfortunately, when this video is made, the only app that can read GeoPDF is only Avenza PDF Maps (available in Android and IOS). The rest is no longer updated (TerraGO GeoPDF) or part of paid services (Global Mapper Mobile Pro). Utilizing GeoPDF for field mapping basemap is very effective and simple. All data in one file. They are structured and tailored according to your setting on the GIS Desktop. They also provide good loading performance as long as the file is not relatively huge. For the second part. I will demonstrate the ArcGIS Mobile Map Package (MMPK) forma...

Here is the code if someone need this,  import arcpy, sys, os, string mxdList = string . split(arcpy . GetParameterAsText( 0 ), ";" ) outloc = arcpy . GetParameterAsText( 1 ) resolution = arcpy . GetParameterAsText( 2 ) georef_info_option = arcpy . GetParameterAsText( 3 ) layers_attributes_option = arcpy . GetParameterAsText( 4 ) for item in mxdList: item = item . strip( ' \' ' ) mxd = arcpy . mapping . MapDocument(item) base = os . path . basename(item) base = os . path . splitext(base)[ 0 ] + ".pdf" pdf_path = os . path . join(outloc, base) # Switch to layout view for df in arcpy . mapping . ListDataFrames(mxd): if df . name == "Layers" : mxd . activeView = df . name mxd . title = df . name # Export to PDF with specified parameters arcpy . mapping . ExportToPDF(mxd, pdf_path, resolution = resolution, georef_info = georef_info_option, layers_attribut...