How do I use ogr2ogr with python GDAL?
Hiking & ActivitiesUnleash the Power of ogr2ogr with Python GDAL: A Human’s Guide Okay, geospatial folks, let’s talk about wrangling vector data. We all know it can be a pain, but GDAL (Geospatial Data Abstraction Library) is like that trusty Swiss Army knife you always reach for. And within GDAL, ogr2ogr is the real MVP for converting
Reloading QGIS Plugin?
Hiking & ActivitiesReloading QGIS Plugins: A Real-World Guide QGIS, that powerhouse of open-source GIS, wouldn’t be half as amazing without its plugins. Seriously, these little add-ons are what let you bend QGIS to your will, tackling everything from niche geospatial tasks to everyday workflows. But what happens when you’re knee-deep in plugin development, or just trying to
Poor Landsat 5 coverage in Google Earth Engine
Hiking & ActivitiesThe Patchy Legacy: Digging into Landsat 5’s Coverage Quirks in Google Earth Engine Landsat 5. What a legend! This satellite wasn’t just hanging around up there; it broke records, earning a spot in the Guinness Book for its incredible run as an Earth observer. Launched way back on March 1, 1984, it kept snapping photos
How make in Python a grid with perfect square cell?
Hiking & ActivitiesPython Grids: Let’s Make Some Perfect Squares! Grids. We see them everywhere, right? From game maps to visualizing data, even tweaking images – they’re super useful. And when those grids are made up of perfect squares? Well, that’s where things get really satisfying. It just simplifies so many calculations and, let’s be honest, looks way
Interpolate grib2 data from GFS
Hiking & ActivitiesDecoding Weather Data: How to Make GFS GRIB2 Files Work for You Ever wondered how weather forecasts are made? A big piece of the puzzle is the Global Forecast System, or GFS. It’s like a giant computer model that spits out tons of data about the atmosphere. This data is stored in something called GRIB2
How to convert lat long coordinates into categorical value in R?
Hiking & ActivitiesFrom Coordinates to Categories: Making Sense of Location Data in R So, you’ve got a pile of latitude and longitude coordinates. Great! But let’s be honest, staring at rows and rows of numbers isn’t exactly insightful. That’s where turning those coordinates into categories comes in. Think of it as grouping locations into neighborhoods or regions