GIS Mapping Software

🛰️ Working with raster and satellite data in Python? These essential libraries will help you streamline your workflows, process imagery, and perform advanced analysis: GDAL 🌍 – The industry standard for reading and writing a wide range of geospatial data formats, including raster data. Rasterio 🗺️ – Designed for reading and writing geospatial raster data, with easy integration into Python workflows. rioxarray 📊 – Makes working with labeled arrays of raster data simpler by extending xarray with geospatial support. SentinelHub-Py 🛰️ – Provides easy access to Sentinel satellite data, making it simple to download and process remote sensing imagery. xarray 📦 – A powerful library for multi-dimensional arrays, ideal for handling satellite and raster datasets with labeled dimensions. satpy ☁️ – Focused on meteorological satellite data processing, especially from platforms like GOES, METEOSAT, and JPSS. EarthPy 🌿 – Perfect for beginners, this library simplifies common tasks for working with Earth and remote sensing data. Apache Sedona 🔥 – A distributed system that provides scalable geospatial analytics, handling large raster and vector data with ease. geemap 🗺️ – Integrates with Google Earth Engine, making it easier to visualize and analyze satellite and remote sensing data in Jupyter notebooks. leafmap 🌍 – A tool for interactive mapping with minimal coding, perfect for visualizing large-scale geospatial data and time-series datasets. elevation 🏔️ – A simple and efficient way to download and work with elevation datasets in Python. EOmaps 🗾 – Enables easy access to Earth Observation data and facilitates interactive plotting of satellite imagery and geospatial data. eoreader 🛰️ – Simplifies downloading and processing Earth Observation data from a variety of sources like Sentinel, Landsat, and others. Forest-at-Risk 🌳 – A tool for analyzing deforestation and forest degradation using satellite imagery and remote sensing data. CoastSat 🏖️ – A tool that uses satellite imagery to extract and monitor changes along coastlines, valuable for coastal erosion studies. HyperCoast 🌊 – Designed to support coastal remote sensing, this library helps to process and analyze coastal environmental data. RasterFrames 🔧 – Brings the power of Apache Spark to raster data processing, allowing scalable geospatial raster analysis. sarpy 🌐 – A library specifically designed for Synthetic Aperture Radar (SAR) data processing, widely used in remote sensing. Whether you’re handling large-scale satellite data, coastal monitoring, or earth observation, these libraries will help you get the most out of your raster data analysis in Python. #gis #moderngis #geospatial #earthobservation #remotesensing #landsat #raster #spatialanalytics

Your GIS maps don't talk to your BIM. Your traffic sensors (IoT) don't inform your emergency response. Your drone footage is just ... sitting on a drive. A City Information Model (CIM) fixes this. I've attached the exact framework that successful smart cities like Helsinki and Singapore use. It's not about more data. It's about connecting the data you already have. Here's the simple, 3-stage breakdown 👇 Stage 1: Data Acquisition This is about cataloguing what you already own. - Geographic Info (GIS): Your maps, roads, and utility lines. - Building Info (BIM): 3D models of new and existing structures. - Sensors (IoT): Traffic, air quality, waste management. - Remote Sensing: Drone and satellite imagery. Right now, these are all in separate "drawers." The goal is to bring them to the same "table." Stage 2: Data Processing This is the most critical step. It’s where you break the silos. - Clean & Standardize: Make all data speak the same language using standards like ISO/OGC. - Fuse & Integrate: This is where GIS + BIM + IoT data are merged. Your 3D building model now "knows" its location on the map and its real-time energy use. - Analyze: Use AI to mine patterns. For example: "This intersection always floods when rainfall exceeds 2 inches, and traffic backs up 3 miles. Let's re-route automatically next time."🖐️ Stage 3: Data Application This is why you did the work. Your connected data is now a tool. You can now finally, visualize (meaningful) in 3D. - Optimize Emergency: Deploy first responders with pinpoint accuracy. - Monitor Environment: Track air quality, noise pollution, or energy use. I've attached this framework for you to consider. --------- Follow me for #digitaltwins Links in my profile Florian Huemer

Recently there have been lots of studies investigating the fusion of SAR and optical satellite imagery for water body and flood mapping. Unfortunately, most of these studies treat SAR images as if they are nothing but additional spectral channels of the optical images. This ignores the fact that the information content and uncertainties are very different for these two data sources. As a result, one obtains maps of surface water extent that are undefined. Is it the total surface water extent? … No, this is hardly ever the case! Or is it the union of surface water areas observable in the optical data and SAR data respectively? More likely, but only if the algorithm favors water detection over other signals, which call for troubles in other places. To address this fundamental problem, Davide Festa, Muhammed Hassaan and I have developed a physics-aware approach for fusing SAR and optical surface water data sets. This allows users of the derived data to understand its limitations, i.e. not only the extent of surface water bodies, but also areas of high uncertainty (e.g. deserts or densely vegetated terrain) and locations where water bodies cannot be observed (e.g. forests or cities). See the preprint here: Festa, D., Hassaan, M., & Wagner, W. (2026) SAR and optical imagery for dynamic global surface water monitoring: Addressing sensor-specific uncertainty for data fusion, SSRN, https://lnkd.in/d-eid9Es One important bonus effect: This approach can be used to fuse existing water body and flood datasets that reside in different data centers, i.e. there is no need to bring all optical and SAR images together on one platform. #SAR #MultiSpectral #Sentinel1 #Sentinel2 #Landsat #WaterBodies #Flood Figure(from the preprint) illustrating the fusion of Sentinel-1 (masked for dense vegetation, topography, etc.) and Sentinel-2 (masked for clouds, forests, etc.) for providing a more complete and more accurate map of surface water extent.

Ground sensors can't cover every watershed, but NASA satellite capabilities can help fill the gap. A NASA-funded project, led by Water Resources PIs at the University of Mississippi has successfully integrated NLDAS-2 EO data into the AIMS decision-support platform. Working with partners from the USDA, this major upgrade empowers farmers, land managers, and decision-makers across the southeast US to easily simulate runoff, water quality, and agricultural impacts, even in remote, unmonitored areas. From EO to real-world action. Learn how satellites and models are transforming watershed management. #NASA #WaterResources #EarthData #Hydrology #Remotesensing #NLDAS

Continuing on the #Urbanization and #DroughtRisk theme, one of the important questions comes up is - How should we do this analysis, when many cities globally may not have the data? We address this need by developing and adopting a data-fusion framework , which is now published as 🏭 "Huang, Shuzhe, et al. "Urbanization-induced spatial and temporal patterns of local drought revealed by high-resolution fused remotely sensed datasets." #RemoteSensingofEnvironment 313 (2024): 114378." 📌 Our findings revealed that urbanization led to more intense peak drought intensity and average drought severity. In addition, urban drought fields showed lower effective radius, indicating more concentrated drought towards urban regions. 📍 From the text ....." we initially proposed a two-step fusion framework, integrating both surface (i.e., gridded data)-surface and point (i.e., in-situ data)-surface fusion. The framework was applied to generate daily precipitation and average/maximum/minimum air temperature at a 1 km resolution through the integration of high-resolution remotely sensed datasets ... 📍 "comparison of our fused data with CPC, ERA5-Land, CMFD, CHIRPS, IMERG, and TMPA products confirmed its capability in capturing local-scale meteorological dynamics by improving spatial resolution from 0.1°-0.25° to 1 km. Utilizing these high-resolution datasets, we quantified urbanization's impacts on local drought across 52 major cities ..." 📍 "We found that urbanization significantly magnified extreme Standardized Precipitation Evapotranspiration Index (SPEI) and drought severity in 69.2% and 61.5% of these cities, respectively. The effects of urbanization on extreme SPEI were amplified by the increase of urbanization rates, with a slope of −0.24 (p < 0.05). To further examine the spatial patterns of urbanization-induced local drought, we proposed a drought spatial field identification method.." #Droughts #IPCC #CityClimate Jackson School of Geosciences at The University of Texas at Austin Cockrell School of Engineering, The University of Texas at Austin University of Texas Center for Space Research #UTcityClimateCoLab

𝐓𝐞𝐫𝐫𝐚𝐅𝐌: 𝐔𝐧𝐢𝐟𝐲𝐢𝐧𝐠 𝐒𝐀𝐑 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐄𝐚𝐫𝐭𝐡 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧 Current EO models face a fundamental limitation: they're often designed for single sensor types, missing the complementary information available when combining radar and optical data. This fragmentation means we can't fully leverage the wealth of satellite observations monitoring our planet. Danish et al. introduced TerraFM, a foundation model that unifies multisensor Earth observation in an unprecedented way. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Earth observation data comes from diverse sensors—optical imagery captures surface details but is limited by clouds and darkness, while SAR radar penetrates clouds and works day-night but provides different information types. Many current models handle these separately, but the real world requires integrated understanding. Climate monitoring, disaster response, and agricultural assessment all benefit from fusing these complementary data streams. 𝐊𝐞𝐲 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬: ◦ 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐬𝐜𝐚𝐥𝐞 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Built on 18.7M global tiles from Sentinel-1 SAR and Sentinel-2 optical imagery, providing unprecedented geographic and spectral diversity ◦ 𝐋𝐚𝐫𝐠𝐞 𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐭𝐢𝐥𝐞𝐬: Uses 534×534 pixel tiles to capture broader spatial context compared to traditional smaller patches, enabling better understanding of landscape-scale patterns ◦ 𝐌𝐨𝐝𝐚𝐥𝐢𝐭𝐲-𝐚𝐰𝐚𝐫𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: Modality-specific patch embeddings handle the unique characteristics of multispectral and SAR data rather than forcing them through RGB-centric designs ◦ 𝐂𝐫𝐨𝐬𝐬-𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐟𝐮𝐬𝐢𝐨𝐧: Dynamically aggregates information across sensors at the patch level, learning how different modalities complement each other ◦ 𝐃𝐮𝐚𝐥-𝐜𝐞𝐧𝐭𝐞𝐫𝐢𝐧𝐠: Addresses the long-tailed distribution problem in land cover data using ESA WorldCover statistics, ensuring rare classes aren't overshadowed 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭𝐬: TerraFM sets new benchmarks on GEO-Bench and Copernicus-Bench, demonstrating strong generalization across geographies, modalities, and tasks, including classification, segmentation, and landslide detection. The model achieves the highest accuracy on m-EuroSat while operating at significantly lower computational cost compared to other large-scale models. 𝐁𝐢𝐠𝐠𝐞𝐫 𝐢𝐦𝐩𝐚𝐜𝐭: TerraFM represents a shift toward unified systems that can seamlessly combine different sensor types to provide more reliable insights. This approach could transform applications from precision agriculture and climate monitoring to disaster response, where the ability to integrate multiple data sources can mean the difference between accurate assessment and missed critical changes. paper: https://lnkd.in/ev_VhSPA code: https://lnkd.in/eQVYrJZV model: https://lnkd.in/eqaeD3dW #EarthObservation #FoundationModels #RemoteSensing #MachineLearning #GeospatialAI

After seeing too many drone datasets sitting unused on hard drives, I built "hovR". Drones capture amazing crop data. But the gap between "raw orthomosaic"  and "publishable scientific result" is enormous: 1. Days of manually drawing plot polygons in QGIS 2. Inconsistent flight quality checks 3. Custom scripts for seasonal integration 4. Ground truth data that never quite aligns with flight dates hovR fills that gap. One R package, five integrated modules, 20+ functions covering the entire middle layer of drone agriculture: 1. FLIGHT QC: 7-metric automated checks + HTML audit reports 2. TEMPORAL: seasonal AUC, derivatives, optimal flight windows, BBCH tagging 3. SEGMENTATION: auto-detect plots OR generate grid from known layout 4. CALIBRATION: automatic reflectance panel detection 5. FUSION: merges drone VI with ground truth using date-lag matching Just validated on a Brazilian spring wheat dataset:19 flights, 4 months, Parrot Sequoia multispectral. Results speak for themselves: - NDVI arch peaked 2018-09-26 at flowering (BBCH 65) — exactly as expected - Senescence onset flagged automatically at the correct week - Seasonal AUC maps exported cleanly to QGIS The big idea: you should not need to write 500 lines of custom code  every time you analyze a drone dataset. Open-source, MIT-licensed, wavelength-agnostic (works with any sensor). How to install? remotes::install_github("Lalitgis/hovR") If you work with drone imagery and this saves you time, I would love  to hear your feedback. The package is still in development phase and user might face some issue during using it, if so, please report you issue to the GitHub issue section. #RStats #DroneMapping #PrecisionAgriculture #RemoteSensing #UAV  #AgTech #OpenSource #CropScience #DataScience #OpenScience

For the past year and slightly beyond, I've been working with Google Earth Engine (GEE) to extract remotely sensed data for modeling environmental attributes. Along the way, I’ve picked up insights that could help others navigating similar challenges, so I’ll be sharing bits and pieces of my experience. Hopefully, this fills some knowledge gaps for those who need it.  GEE is a powerful tool for Remote Sensing, allowing users to extract, analyze, and manipulate a vast repository of geospatial data without the hassle of downloading massive raster (GeoTIFF) files. Whether you’re working with satellite imagery or RADAR data, GEE gives you quick access to various datasets using Python (which I use more often) or JavaScript. Back in my early days with Remote Sensing, downloading satellite imagery meant manually searching for path and row coordinates, dealing with large files, and worrying about storage constraints. But with even intermediate Python skills, I can now query and extract exactly what I need in real time — no downloads required. This has completely transformed how I work with data. To use GEE in Python, the go-to package is "ee", but before installing it, you’ll need to set up a Google Cloud project. Once that’s done:  1.            A Google cloud project needs to be created before the installation of the package. 2.            Install the ee package in python. 3.            Initialize Earth Engine each time you restart your program or console.   Once everything is set up, extracting data is simple. A quick search for something like "Landsat 8 Google Earth Engine" will give you the direct link to access its properties, bands, and metadata for further processing. One thing to note: Earth Engine initialization isn’t required every time — unless the software is uninstalled, or major API changes are made to your cloud project. Otherwise, once it’s authenticated, you’re good to go. I'll be sharing more on working with satellite imagery, running spatial analysis and machine learning models using Python in my next posts. If this is something you’re working on or curious about, let’s discuss in the comments! #GEE #Python #RemoteSensing #EarthEngine #GeospatialData

I’m excited to share highlights from my recent presentation during the drone school under the GEANTech on “#Hybrid #Drone‑#Satellite Systems for Advanced #Irrigation Water #Management”, where we explored how cutting‑edge remote sensing and #data‑#fusion techniques can revolutionize precision agriculture. 🔹 Why hybrid systems? By combining high‑resolution UAV imagery (RGB, multispectral & thermal) with multispectral satellite data (Sentinel‑2, Landsat), we get both the fine #spatial detail and broad #temporal coverage needed to monitor crop health and water stress at scale. 🔹 Data Fusion & AI: • #Multi‑scale fusion calibrates drone data to satellites, ensuring model consistency • #Machine #learning algorithms automate the processing of fused imagery for real‑time insights • #Decision‑support systems translate these insights into actionable irrigation schedules 🔹 Case studies: • Italian vineyards: NDVI‑derived maps guided autonomous irrigation, cutting water use by 20% while improving vine vigor • Tunisian olive groves: Targeted interventions in water‑stress zones boosted yield resilience under arid conditions 🔹 #Challenges & next steps: • Overcoming sensor‑format heterogeneity & regulatory constraints • Reducing costs for smallholder adoption • Scaling up with drone swarms, IoT integration & AI‑driven predictive models A big thank you to everyone who joined the discussion and shared valuable questions—your engagement drives innovation forward! 💧🚁🛰️ #PrecisionAgriculture #RemoteSensing #GeoAI #IrrigationInnovation #Sustainability