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Shapely 2.0.0 Released
By Lurker,
Shapely 2.0 version is a major release featuring a complete refactor of the internals and new vectorized (element-wise) array operations, providing considerable performance improvements (based on the developments in the PyGEOS package), along with several breaking API changes and many feature improvements.
Refactor of the internals#
Shapely wraps the GEOS C++ library for use in Python. Before 2.0, Shapely used ctypes to link to GEOS at runtime, but doing so resulted in extra ove
Landsat Next
By Lurker,
Landsat Next is on the horizon—the new mission will not only ensure continuity of the longest space-based record of Earth’s land surface, it will fundamentally transform the breadth and depth of actionable information freely available to end users.
Landsat Next will also provide new capabilities for the next generation of Landsat users. The enhanced spatial and temporal resolution of the 26-band “superspectral” Landsat Next constellation will unlock new applications for water qu
All Landsat Collection 1 data will become unavailable on December 30, 2022.
By Lurker,
This removal includes all Collection 1 Level-1, Level-2, Level-3, and ESPA- related Landsat 1-8 products. Collection 1 has not been updated with Landsat products since December 31, 2021 and does not include Landsat 9 data.
Users are encouraged to migrate their workflow to Landsat Collection 2 as soon as possible. Due to advancements in data processing and algorithm development, users are discouraged from using Collection 1 and Collection 2 interchangeably within the same workflow.
Land
QField 2.5 is here, reaching new heights
By Lurker,
QField’s main new feature of this 2.5 release cycle is its brand new elevation profiling functionality which has been added to the measuring tool. Users are now able to dynamically build and analyze elevation profiles wherever they are – in the field or on their desktop – by simply drawing paths onto their maps and projects.
This is a great example of QField’s capability at bringing the power of QGIS through a UI that keeps things simple and avoids being in your way until you need it. Oh an
HISTARFM - How to Work with Gap-Filled Imagery
By Lurker,
Although there is an extensive array of optical remote sensing sensors from a variety of satellites providing long time data records, their data are incapable of retrieving reliable land surface information when clouds, aerosols, shadows, and strong angular effects are present in the scenes. The mitigation of noise and gap filling of satellite data are preliminary and almost mandatory tasks for any remote sensing application aimed at effectively analyzing the earth's surface continuously through
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Are you ready to level up your geospatial skills? Join our comprehensive training course covering ArcMap, ArcGIS Pro, and ArcOnline—the essential tools for modern spatial analysis and programming! What You’ll Learn: Core functionalities of ArcMap & ArcGIS Pro Cloud-based mapping with ArcGIS Online Automating workflows with Python & ModelBuilder Creating interactive web maps & apps Who Should Enroll? GIS beginners & professionals Urban planners, environmental scientists, & data analysts Developers looking to integrate spatial programming Why Choose This Course? Hands-on projects & real-world applications Expert-led sessions & flexible learning Limited slots available! Click here to register. Let’s shape the future of spatial data together! -
GPS is an incredible piece of modern technology. Not only does it allow for locating objects precisely anywhere on the planet, but it also enables the turn-by-turn directions we take for granted these days — all without needing anything more than a radio receiver and some software to decode the signals constantly being sent down from space. [Chris] took that last bit bit as somewhat of a challenge and set off to write a software-defined GPS receiver from the ground up. As GPS started as a military technology, the level of precision needed for things like turn-by-turn navigation wasn’t always available to civilians. The “coarse” positioning is only capable of accuracy within a few hundred meters, so this legacy capability is the first thing that [Chris] tackles here. It is pretty fast, though, with the system able to resolve a location in 24 seconds from cold start and then displaying its information in a browser window. Everything in this build is done in Python as well, meaning that it’s a great starting point for investigating how GPS works and for building other projects from there. The other thing that makes this project accessible is that the only other hardware needed besides a computer that runs Python is an RTL-SDR dongle. These inexpensive TV dongles ushered in a software-defined radio revolution about a decade ago when it was found that they could receive a wide array of radio signals beyond just TV. source: Hackaday and GitHub - chrisdoble/gps-receiver
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