Geospatial Analysis using Python and QGIS Training Course Course Overview: This is a 5-day course offered by IRES designed to introduce participants to Geospatial Analysis using Python and QGIS. The course will provide practical knowledge and hands-on experience in using Python for geospatial data analysis and automating geospatial tasks within the QGIS environment. Participants will learn how to manipulate spatial data, perform advanced analyses, and automate workflows to support decision-making processes across a variety of sectors such as urban planning, environmental monitoring, and disaster management. Course Duration: 5 days Personal Impact: Master the integration of Python scripting with QGIS for efficient geospatial data processing. Develop skills in automating common GIS tasks using Python. Gain hands-on experience with key Python libraries for spatial analysis (such as GeoPandas and Shapely). Learn to analyze, visualize, and manipulate spatial data with QGIS, enhancing your GIS workflows. Organizational Impact: Improve the ability to automate geospatial data tasks, saving time and resources. Enhance decision-making processes through advanced spatial analysis. Provide advanced geospatial analysis capabilities within the organization using QGIS and Python. Empower teams with skills to manipulate and analyze spatial data for improved operational performance. Course Objectives: To introduce the fundamentals of Python for geospatial analysis. To equip participants with the knowledge to use QGIS for spatial data visualization and manipulation. To teach participants how to integrate Python with QGIS to automate workflows and enhance data processing. To provide practical experience in geospatial analysis, including working with spatial data formats, performing spatial queries, and generating geospatial reports. To empower participants to apply their knowledge to real-world projects, including environmental monitoring and urban planning. Course Outline: Module 1: Introduction to Geospatial Analysis with Python and QGIS Overview of geospatial data types and formats (e.g., shapefiles, GeoJSON, raster data) Introduction to QGIS interface and basic functionalities (layer handling, map rendering, etc.) Introduction to Python in GIS: key libraries (GeoPandas, Shapely, Fiona, Rasterio) Setting up the development environment: installing QGIS and Python libraries Case Study 1: Use of geospatial analysis in environmental monitoring (e.g., deforestation mapping) Hands-On Exercise 1: Loading and visualizing spatial data in QGIS. Module 2: Python Scripting for Geospatial Data Manipulation Introduction to GeoPandas: manipulating vector data with Python Loading, reading, and writing spatial data using GeoPandas Spatial operations: buffering, merging, and intersecting geometries Working with spatial indexes for faster querying Case Study 2: Spatial analysis for land use planning and urban development Hands-On Exercise 2: Using GeoPandas to read and process shapefiles, perform basic spatial operations. Module 3: Spatial Data Visualization and Analysis with Python and QGIS Visualization techniques: working with maps, styling layers, and adding attributes in QGIS Visualizing geospatial data with Matplotlib and Plotly Spatial analysis in QGIS: buffering, clipping, and overlay analysis Conducting proximity analysis and spatial queries using QGIS and Python Case Study 3: Using spatial analysis for disaster management (e.g., flood zone mapping) Hands-On Exercise 3: Visualizing and analyzing a sample geospatial dataset (buffer analysis, heatmap generation). Module 4: Advanced Geospatial Analysis in QGIS with Python Integration Automating common spatial analysis tasks using Python scripts Working with raster data in QGIS: analysis using Rasterio and PyQGIS Performing advanced geospatial analyses (e.g., spatial joins, interpolation) Writing custom Python scripts to automate geospatial analysis workflows in QGIS Case Study 4: Environmental impact assessment using raster-based analysis Hands-On Exercise 4: Writing Python scripts for raster analysis, such as land cover classification or suitability modeling. Module 5: Automating and Extending QGIS with Python Using QGIS Python console and PyQGIS for advanced automation Creating custom QGIS plugins using Python for repetitive spatial tasks Integrating external data sources (e.g., APIs, web services) into QGIS projects with Python Performance optimization: Efficient handling of large spatial datasets in QGIS and Python Case Study 5: Building a custom QGIS plugin for automated spatial report generation Hands-On Exercise 5: Developing a simple Python-based QGIS plugin to automate a common GIS task (e.g., buffer creation).

Hello! Hello!   Join us on Wednesday [GIS DAA] for a zoom webinar as we discuss "The Future of GIS: Emerging Technologies and Global Impact in Different Sectors" and "GIS for Sustainable Development."   Our event will feature two distinguished key speakers, senior specialists in the field, who will enlighten us with their insights and expertise.   Save the date and stay tuned for more details on this exciting opportunity to explore the cutting-edge advancements and societal applications of Geographic Information Systems. Here is the registration link: https://shorturl.at/f5W68   See you there! #IRESexperience

Sometimes you need to create a satellite navigation tracking device that communicates via a low-power mesh network. [Powerfeatherdev] was in just that situation, and they whipped up a particularly compact solution to do the job. As you might have guessed based on the name of its creator, this build is based around the ESP32-S3 PowerFeather board. The PowerFeather has the benefit of robust power management features, which makes it perfect for a power-sipping project that’s intended to run for a long time. It can even run on solar power and manage battery levels if so desired. The GPS and LoRa gear is all mounted on a secondary “wing” PCB that slots directly on to the PowerFeather like a Arduino shield or Raspberry Pi HAT. The whole assembly is barely larger than a AA battery. It’s basically a super-small GPS tracker that transmits over LoRa, while being optimized for maximum run time on limited power from a small lithium-ion cell. If you’re needing to do some long-duration, low-power tracking task for a project, this might be right up your alley. https://hackaday.com/2024/10/17/tiny-lora-gps-node-relies-on-esp32/

Multiple motors or servos are the norm for drones to achieve controllable flight, but a team from MARS LAB HKU was able to a 360° lidar scanning drone with full control on just a single motor and no additional actuators. Video after the break. The key to controllable flight is the swashplateless propeller design that we’ve seen a few times, but it always required a second propeller to counteract self-rotation. In this case, the team was able to make that self-rotation work so that they could achieve 360° scanning with a single fixed LIDAR sensor. Self-rotation still needs to be slowed, so this was done with four stationary vanes. The single rotor also means better efficiency compared to a multi-rotor with similar propeller disk area. The LIDAR comprises a full 50% of the drone’s weight and provides a conical FOV out to a range of 450m. All processing happens onboard the drone, with point cloud data being processed by a LIDAR-inertial odometry framework. This allows the drone to track and plan its flight path while also building a 3D map of an unknown environment. This means it would be extremely useful for indoor or underground environments where GPS or other positioning systems are not available. All the design files and code for the drone are up on GitHub, and most of the electronic components are off-the-shelf. This means you can build your own, and the expensive lidar sensor is not required to get it flying. This seems like a great platform for further experimentation, and getting usable video from a normal camera would be an interesting challenge.   Single Rotor Drone Spins For 360 Lidar Scanning | Hackaday