GIS Mapping Software

🌊 𝗡𝗲𝘄 𝗙𝗹𝗼𝗼𝗱 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗔𝗿𝗰𝗚𝗜𝗦 𝗣𝗿𝗼 𝟯.𝟱 🌍 Flood simulation in ArcGIS is crucial for risk assessment, disaster preparedness, and urban planning. It enables geospatial professionals to model flood scenarios based on real-world data, helping decision-makers understand potential impacts on infrastructure, communities, and ecosystems. With advanced tools in ArcGIS Pro 3.5, simulations can incorporate dynamic rainfall, terrain infiltration, terrain roughness and more to refine predictions and improve mitigation strategies. This enhances emergency response, reduces damage costs, and supports sustainable development. 🌎 And this tool just got great upgrades! With ArcGIS Pro 3.5, creating flood simulation scenarios is more intuitive, dynamic, faster and more precise. 🚀 My personal highlights: 🔹𝗦𝘂𝗿𝗳𝗮𝗰𝗲 𝗥𝗼𝘂𝗴𝗵𝗻𝗲𝘀𝘀 𝗥𝗮𝘀𝘁𝗲𝗿: Now it’s possible to define the roughness of the surface which influences water flow! 🔹𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 “𝗪𝗮𝘁𝗲𝗿 𝗦𝗽𝗲𝗲𝗱”: Water Speed now can be visualized in the Symbology pane! 🔹𝗜𝗻𝘀𝗲𝗿𝘁 “𝗦𝗶𝗻𝗸 𝗔𝗿𝗲𝗮𝘀”: Water Speed now can be visualized in the Symbology pane! 🔹𝗣𝗹𝗮𝘆𝗯𝗮𝗰𝗸 𝗥𝗮𝘁𝗲: Now you can define the playback rate in different fps. Flood simulation in ArcGIS is a powerful tool with diverse applications across industries. Here are some key use cases: 🌍 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 🔸 Predict flood-prone areas and develop evacuation plans for communities. 🔸 Optimize placement of rescue resources and improve response coordination. 🔸 ... 🏗️ 𝗨𝗿𝗯𝗮𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 🔸 Design flood-resistant transport networks and drainage systems. 🔸 Identify vulnerable buildings and assets to strengthen resilience. 🔸 ... 🌿 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗜𝗺𝗽𝗮𝗰𝘁 & 𝗖𝗼𝗻𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 🔸 Assess the effects of flooding on wetlands, rivers, and ecosystems. 🔸 Model sediment and pollutant transport to ensure water quality protection. 🔸  ... 🛡️ 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 & 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 🔸 Improve flood risk predictions for property insurance pricing. 🔸 Enhance data-driven decision-making for risk mitigation investments. 🔸 ... With ArcGIS Pro 3.5, flood simulation becomes even more precise and actionable, empowering industries to mitigate risks and make informed decisions. See the technical paper for more information ➡️ https://lnkd.in/d3u37-Ey 🌊💡 🤝♻️ Let's spark a conversation! How are you leveraging flood simulation tools in ArcGIS? Let’s connect and exchange ideas! Drop your insights below 👇 💡 🌟 #Esri #GIS #DigitalElevationModels #SpatialAnalysis #ArcGIS #remotesensing #flood #floodmodelling #rainfall #climatechange #FloodManagement #DisasterResponse #UrbanPlanning #Sustainability #EsriDeutschland #mapping #ArcGISPro #esrivoices🔍 🚀 🌱

What is a 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲? With the rise of Foundational Models, Vector Databases skyrocketed in popularity. The truth is that a Vector Database is also useful outside of a Large Language Model context. When it comes to Machine Learning, we often deal with Vector Embeddings. Vector Databases were created to perform specifically well when working with them: ➡️ Storing. ➡️ Updating. ➡️ Retrieving. When we talk about retrieval, we refer to retrieving set of vectors that are most similar to a query in a form of a vector that is embedded in the same Latent space. This retrieval procedure is called Approximate Nearest Neighbour (ANN) search. A query here could be in a form of an object like an image for which we would like to find similar images. Or it could be a question for which we want to retrieve relevant context that could later be transformed into an answer via a LLM. Let’s look into how one would interact with a Vector Database: 𝗪𝗿𝗶𝘁𝗶𝗻𝗴/𝗨𝗽𝗱𝗮𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮. 1. Choose a ML model to be used to generate Vector Embeddings. 2. Embed any type of information: text, images, audio, tabular. Choice of ML model used for embedding will depend on the type of data. 3. Get a Vector representation of your data by running it through the Embedding Model. 4. Store additional metadata together with the Vector Embedding. This data would later be used to pre-filter or post-filter ANN search results. 5. Vector DB indexes Vector Embedding and metadata separately. There are multiple methods that can be used for creating vector indexes, some of them: Random Projection, Product Quantization, Locality-sensitive Hashing. 6. Vector data is stored together with indexes for Vector Embeddings and metadata connected to the Embedded objects. 𝗥𝗲𝗮𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮. 7. A query to be executed against a Vector Database will usually consist of two parts: ➡️ Data that will be used for ANN search. e.g. an image for which you want to find similar ones. ➡️ Metadata query to exclude Vectors that hold specific qualities known beforehand. E.g. given that you are looking for similar images of apartments - exclude apartments in a specific location. 8. You execute Metadata Query against the metadata index. It could be done before or after the ANN search procedure. 9. You embed the data into the Latent space with the same model that was used for writing the data to the Vector DB. 10. ANN search procedure is applied and a set of Vector embeddings are retrieved. Popular similarity measures for ANN search include: Cosine Similarity, Euclidean Distance, Dot Product. Some popular Vector Databases: Qdrant, Pinecone, Weviate, Milvus, Faiss, Vespa. How are you using Vector DBs? Let me know in the comment section! #MachineLearning #GenAI #LLM #AI

🌊🔍Exploring Flood Impact Analysis and Visualization with ArcGIS Pro🌍✨. By Matthias S.. Original post: Flooding is one of the most devastating natural disasters, exacerbated by climate change, impacting communities, economies, and environments. With the power of ArcGIS Pro, we can conduct comprehensive flood impact analyses and create stunning visualizations that help us understand and mitigate these risks. "... visible danger is the best argument for prevention - this also applies in digital worlds ..." 🌡️ Climate Change and Flooding: Climate change is leading to increased rainfall (in some areas to decreased as well), rising sea levels, and more frequent extreme weather events, resulting in heightened flood risks. Understanding these changes is crucial for effective planning and response. 📈 Key Benefits of Using ArcGIS Pro for Flood Analysis: 1️⃣ Data Integration: Combine various datasets, including elevation, land use, climate models, and historical flood events, to create a robust analysis. 2️⃣ 3D Visualization: Utilize 3D capabilities to visualize flood extents and impacts on infrastructure and communities, considering future climate scenarios. 3️⃣ Scenario Modeling: Simulate different flood scenarios under varying climate conditions to assess potential impacts and plan effective responses. 4️⃣ Hydrological Analysis Tools: Use tools like the Hydrology toolset to analyze watershed dynamics and flood risk. 5️⃣ Remote Sensing: Leverage satellite imagery and remote sensing data to monitor changes in land cover and water bodies due to climate change. 6️⃣ Community Engagement: Share interactive maps and visualizations with stakeholders to raise awareness and drive action. By leveraging these tools, we can enhance our preparedness and response strategies, ultimately saving lives and reducing economic losses. 💪🌈 🤝 Let's spark a conversation! How are you leveraging ArcGIS for flood Analysis? Share your insights, challenges, and success stories below. Let's amplify our collective GIS capabilities! 💬💡 💡 🌟 #FloodAnalysis #ClimateChange #DataVisualization #Resilience #FloodManagement #ArcGISPro #RiskMitigation #Esri #GIS #SpatialAnalysis #ArcGIS #flood #climatechange #FloodManagement #DisasterResponse #UrbanPlanning #Sustainability #ClimateChangeAdaption #EsriDeutschland #ArcGISPro #esrivoices🔍 🚀 🌱

⚙️ The first part of the Geospatial Data Stack is the transformation layer which sits vertically across the stack as it integrates across every layer. And of course, it handles data transformations. What I specifically mean by transformations is taking one form or format of data and changing it into another format. This doesn't account for in-dataset transformations such as changing datasets, aggregations, etc. Let's go through the list: 🛰️ First is GDAL, the backbone of so many other geospatial tools to turn one data type into another. Everything from Geopandas, QGIS, rasterio, and many other tools depend on it. It is the unsung hero for the Geospatial Data Stack. dbt Labs provides the ability to handle the transformation of data when it lands within a database or data warehouse and in the case of geospatial can handle SQL transformations to ensure you have properly constructed geometries, among other things. You can also add in Airbyte to orchestrate your data movement from 100s of sources and integrate dbt to handle the transformations. Airbyte is a pure ELT tool that allows you to move data from sources like CSV or JSON in addition to APIs or more sources like Salesforce or GitHub. BigGeo has added some very interesting capabilities for enabling the transformation of geospatial data into common data warehouses and cloud platforms using indexing to achieve far faster spatial query speed in those platforms, and more features coming soon. Finally H3 is another foundational transformation toolkit across many languages allowing for common indexing of many spatial data types into a single, interoperable format. #gis #moderngis #geospatial #spatialanalytics #geospatialdataengineering #dataengineering #earthobservation #spatialsql

🚀Roadmap for Modern GIS Mastery🌍 As geospatial technologies continue to evolve, I’ve crafted a structured roadmap to guide you through a modern, holistic GIS learning journey. Whether you're just starting out or looking to deepen your expertise, this pathway covers key areas in today’s GIS world: 🔹 Phase 1: Foundations of GIS Understand spatial data types, coordinate systems, and master tools like QGIS, ArcGIS, and Google Earth Engine. 🔹 Phase 2: Data Acquisition & Management  Learn to collect, clean, and manage spatial data. Dive into spatial databases like PostGIS and GeoPackages. 🔹 Phase 3: Spatial Analysis & Cartography Master vector/raster analysis, terrain modeling, and create powerful visualizations and maps. 🔹 Phase 4: Remote Sensing & Earth Observation  Explore satellite imagery, spectral indices (like NDVI), and classification using tools like SNAP and Earth Engine. 🔹 Phase 5: GIS Programming & Automation Automate workflows using Python (`geopandas`, `rasterio`) or R. Use PyQGIS, ModelBuilder, and GDAL for power scripting. 🔹 Phase 6: Spatial Modeling & Statistics Dig into geostatistics, spatial ML, hydrological and land-use modeling with tools like SWAT and HEC-HMS. 🔹 Phase 7: Web & Cloud GIS Build web maps using Leaflet, Mapbox, ArcGIS Online, or GeoServer. Take GIS to the cloud. 🔹 Phase 8: Specialization Tracks Pick your passion: Urban Planning, Environment, Agriculture, Disaster Management, Health, Business, or 3D GIS. 🔹 Phase 9: Portfolio Development Build real-world projects, dashboards, and interactive web apps to showcase your skills. 🔹 Phase 10: Certification & Community  Earn certifications (Esri, GISP), join conferences, contribute to open-source, and stay connected with the global GIS community. 💡 The world needs spatial thinkers. If you’re in the field or just starting out, let’s connect and grow together! 🌐 #gis #geospatial #remotesensing #earthobervation #QGIS #arcgis #pythonGIS #python #webgis #webGIS #Mapping #dataScience #UrbanPlanning #hydrology #ML #AI #jobs #Career

🌐 How do you plan for the unthinkable? Hawai‘i’s award-winning Geospatial Decision Support System (GDSS) is transforming how we approach disaster preparedness. Using GIS mapping, the tool identifies the relationships between energy infrastructure and the lifelines that keep our communities functioning. 💡 Here’s why it’s a game-changer: -It calculates the risk of disruptions to critical infrastructure like substations, pipelines, and power plants. -It visualizes cascading impacts, helping us understand which systems are most vulnerable to flooding, high winds, or other disasters. -It prioritizes actions to protect the most vital links in our infrastructure chain. For Hawai‘i, this means smarter strategies to strengthen our grid and protect our communities. For the rest of the world, this is a lesson in using data to drive resilience. Mahalo to the team at the Hawaii State Energy Office for their hard work in making this tool available. What other regions could benefit from such a proactive approach? Let’s discuss in the comments! 👇 #GIS #Microgrids #EnergyInnovation #ResilientCommunities #AJPerkins #MicrogridMentor

🗺️ From zero to GIS pro - here's the roadmap I wish I had when I started! After years in the geospatial field, I've mapped out the complete learning path for anyone wanting to break into GIS. 🎯 Start with Foundations Don't skip coordinate systems and map projections. I see too many people struggle later because they jumped straight into software. 🛠️ Master Core GIS ArcGIS Pro and QGIS are your bread and butter. Focus on spatial analysis and geoprocessing - that's where the real value is. 💻 Programming Changed Everything Python (ArcPy), SQL/PostGIS, and JavaScript opened up massive career opportunities for me. This is where you separate yourself from the pack. 🌐 Web GIS is the Future Learning ArcGIS Online and web mapping made me much more valuable to employers. The industry is shifting online fast. 🚀 Pick Your Specialization After exploring different areas, I found my niche. Choose yours: Urban Planning, Environmental, Remote Sensing, Cartography, or Enterprise GIS. What I've learned: Start with free tools, build real projects, join GIS communities, and never stop learning! 🎯 Where are you on this journey? Drop a comment - I love connecting with fellow GIS professionals! Save this roadmap for your GIS learning journey! 🔖 #GIS #Geospatial #CareerDevelopment #Mapping #SpatialAnalysis #GISJobs #QGIS #ArcGIS #Python #WebMapping

🌏 🛰️ Supervised Land Use Classification Using Remote Sensing and Machine Learning 🛰️ 🌏 Land use classification is critical in understanding how human activities shape the environment and guiding sustainable development. In this learning experience, I explored how satellite imagery, field data, and machine learning techniques can be combined to generate detailed land use maps for effective spatial analysis and decision-making. 📊 Methodology ✔️ Field Data Collection- Collected 60 ground truth points across five land use classes (Built-up, Vegetation, Paddy, Bare Land, and Water Bodies) using QField linked with QGIS. ✔️ Satellite Data Processing- Processed Landsat 8 imagery in Google Earth Engine (GEE) to extract spectral signatures for each land use class. ✔️ Classification Model- Applied the Support Vector Machine (SVM) algorithm in Google Colab for supervised classification. ✔️ Accuracy Assessment- Validated the classification using a confusion matrix, user/producer accuracy metrics, and the kappa coefficient from both manually and Google Colab. 📈 Results ▪️ The model achieved an overall classification accuracy of 65% with moderate agreement (Kappa = 0.55). ▪️ Built-up area showed higher classification accuracy (70.59 % producer accuracy, 66.67% user accuracy), while Bare Land and Paddy classes had relatively lower accuracy, highlighting areas for future improvement. 🔎 Why This Matters This study demonstrates how integrating ground truth data, satellite imagery, and open-source tools enables practical, cost-effective land use classification, especially valuable for resource and data-limited areas. These insights can guide urban planning, agriculture management, environmental protection, and disaster risk planning. By applying machine learning in geospatial analysis, this study helps connect data to real-world solutions, making it a valuable approach for building more sustainable and resilient communities. #GIS #RemoteSensing #LandUseClassification #MachineLearning #GoogleEarthEngine #QField #QGIS #SpatialData #GeospatialAnalysis #UrbanPlanning #SustainableDevelopment #DataDrivenPlanning

🌍 Generating a High-Resolution 10m DEM Using Sentinel-1 SAR Data 🛰️ Digital Elevation Models (DEMs) are essential for understanding terrain, modeling water flow, and assessing flood risks. In this project, I used Sentinel-1 Synthetic Aperture Radar (SAR) data to create a high-resolution 10-meter DEM, showcasing the power of remote sensing for geospatial analysis. 🔍 Workflow Overview: 🗂️ Data Acquisition: Downloaded Sentinel-1 SLC images from the Copernicus Open Access Hub and verified perpendicular and temporal baselines using the Alaska Satellite Facility (ASF). 🛠️ DEM Generation: Processed the data in SNAP, including coregistration, interferogram formation, phase filtering, and phase unwrapping to ensure high accuracy and detailed elevation modeling. 🗺️ Final DEM Validation: Used ArcGIS for visual inspection, clipping to the study area, and creating final elevation maps with hydrological features like water flow networks. This project highlights the incredible speed, reliability, and precision of Sentinel-1 SAR data for DEM generation. I’m excited to share the detailed step-by-step guide I created — from data collection to final map production — to help others navigate this process and generate their own high-resolution DEMs. #RemoteSensing #GIS #Sentinel1 #DEM #Geospatial #EarthObservation #ArcGIS #SNAP #SARData #SpatialAnalysis #Mapping

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗥𝗲𝗰𝗶𝗽𝗿𝗼𝗰𝗮𝗹 𝗟𝗲𝘃𝗲𝗹𝗶𝗻𝗴 📍 (𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰 𝗘𝗿𝗿𝗼𝗿𝘀 𝗶𝗻 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗛𝗲𝗶𝗴𝗵𝘁 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁) _... Achieving precise results can be challenging when measuring across obstacles like rivers or valleys i.e., where setting up a level equidistant between two points is impractical. This is where Reciprocal Leveling applies—a method designed to eliminate systematic errors due to collimation, curvature, and atmospheric refraction. 🔹𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗕𝗲𝗵𝗶𝗻𝗱 𝗥𝗲𝗰𝗶𝗽𝗿𝗼𝗰𝗮𝗹 𝗟𝗲𝘃𝗲𝗹𝗶𝗻𝗴 🎯 In standard leveling, errors can be minimized by ensuring equal backsight and foresight distances. However, when a natural obstruction like a river prevents this, reciprocal observations are necessary. By taking measurements from both sides of the river or valley, the inherent errors cancel out, yielding an accurate elevation difference. 🔹𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗘𝗿𝗿𝗼𝗿 𝗖𝗮𝗻𝗰𝗲𝗹𝗹𝗮𝘁𝗶𝗼𝗻 🔍 - Consider two points, 𝗔 and 𝗕, on opposite banks of a river: 1️⃣ First, a level is set near 𝗔, and 𝗕 staff readings 𝗮₁(at 𝗔) and 𝗯₁ (at 𝗕) are recorded. The apparent difference in height includes a systematic error 𝗲. 2️⃣ Next, the level is repositioned near 𝗕, and readings 𝗮₂ (at 𝗔) and 𝗯₂ (at 𝗕) are recorded. Again, the same error e is present. - By deriving the true elevation difference (h) from both sets of readings: 𝗵 = [(𝗮₁ - 𝗯₁) + (𝗮₂ - 𝗯₂)] ÷ 𝟮 - This equation demonstrates that the systematic error cancels out, leaving only the true difference in elevation. 🔹𝗪𝗵𝘆 𝗥𝗲𝗰𝗶𝗽𝗿𝗼𝗰𝗮𝗹 𝗟𝗲𝘃𝗲𝗹𝗶𝗻𝗴 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 🤔 1. Eliminates errors from collimation, curvature, and refraction. 2. Improves accuracy in differential height measurements over obstacles. 3. Essential for high-precision surveying in geospatial and engineering applications. 🔹𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 🛠️ • River Cross-Section Profiling • Infrastructure Development (Bridges, Dams) • Hydrological and Floodplain Studies • Topographic Mapping Across Uneven Terrain Share 🫵🏾 perspective 👇🏾 🔹Also Check: ▪️"𝗢𝗽𝘁𝗶𝗰𝗮𝗹 𝗘𝗿𝗿𝗼𝗿𝘀 𝗶𝗻 𝗠𝗼𝗱𝗲𝗿𝗻 𝗟𝗶𝗻𝗲𝗮𝗿 𝗦𝘂𝗿𝘃𝗲𝘆 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗲𝘀" ----https://lnkd.in/dMmyu8mF 🔻Follow: Gensre Engineering & Research #GeospatialEngineering #Surveying #LevelingTechniques #EngineeringDesign #Topography #Hydrology #Geodesy #RemoteSensing #GIS #LandSurveying Image Credit 📸: ----https://lnkd.in/dMbyQWdQ