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Interactive Satellite Image Experiments for Public Engagement

Private companies, not just governments, control some satellite constellations, with Satellogic being one of them. They operate CubeSats equipped with Earth-imaging technology, and are currently leading the pack.

Private companies dominate the satellite sector nowadays, as governments are no longer the sole...
Private companies dominate the satellite sector nowadays, as governments are no longer the sole operators. Satellogic, for instance, manages a fleet of CubeSats equipped with Earth-imaging devices, leading the industry in...

Interactive Satellite Image Experiments for Public Engagement

Get ready for some space exploration without breaking the bank! Instead of relying on government-owned satellite constellations, there's Satellogic, a company operating CubeSats packed with Earth imaging payloads. And the best part? They offer an open dataset for you to play with.

Let's hop on a journey with Mark Litwintschik as our guide. First things first, you'll need a script to locate those satellites. Next, dive into the huge S3 bucket housing the data, where you'll face a big data question – parsing the metadata. Once you've processed the data, store it in a database. Now you can easily run queries, just like Mark did with Qatar as his example.

The dataset isn't complete, but you never know, your corner of the world might be among the areas in the dataset. You can explore it all using the tools you've just learned. It’s a major upgrade from those low-res weather satellite images.

But hey, where's the fun in stopping there? You can also compare Satellogic's open data with low-resolution weather satellite imagery. Gather the data from sources like NOAA, EUMETSAT, or NASA. Since Satellogic images will likely be higher resolution, you'll need to resample or reproject the data to match the lower resolution for comparison. Upon doing so, take note of differences in detail, spectral bands, and temporal resolution.

Here is a code example to help you visualize the satellite images:

```pythonimport rasterioimport matplotlib.pyplot as plt

with rasterio.open('satellogic.tif') as src: satellogic_img = src.read(1) plt.imshow(satellogic_img, cmap='viridis') plt.title('Satellogic Earth Imagery') plt.show()

with rasterio.open('modis.tif') as src: modis_img = src.read(1) plt.imshow(modis_img, cmap='gray') plt.title('Weather Satellite Imagery') plt.show()```Lastly, bear in mind that Satellogic's primary business is commercial imagery, so their public dataset availability might be limited. Established sources like NOAA and NASA are a reliable starting point for weather satellite data.

Stay tuned for more insights on working with remote sensing data and satellite imagery. Happy exploring! 🚀🛰️

Delve deeper into data-and-cloud-computing by developing a script to locate and interact with the Satellogic CubeSats. You'll need technology like Python and libraries such as rasterio and matplotlib to manage and visualize the Earth imaging data stored in cloud servers, like Amazon S3.

To draw comparison, collect and process low-resolution weather satellite imagery from sources like NOAA, EUMETSAT, or NASA. Use the script to resample or reproject the data and then analyze differences in detail, spectral bands, and temporal resolution using the technology available.

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