Map digitization is an important process that converts maps into digital formats that can be used for further analysis. This process typically requires a deep human involvement because of the need for interpretation and decision-making when translating complex features. With the advancement of artificial intelligence, there is an alternative to conducting map digitization with the help of machine learning techniques. Deepness, or Deep Neural Remote Sensing, is an advanced AI-driven tool designed and integrated as a plugin in QGIS application. This research focuses on assessing the effectiveness of Deepness in automated digitization. This study analyses AI-generated digitization results from Google Earth imagery and compares them with digitized outputs from OpenStreetMap (OSM) to evaluate performance.

Remote sensing has entered a new era with the rapid development of artificial intelligence approaches. However, the implementation of deep learning has largely remained restricted to specialists and has been impractical because it often requires (i) large reference datasets for model training and validation; (ii) substantial computing resources; and (iii) strong coding skills. Here, we introduce IAMAP, a user-friendly QGIS plugin that addresses these three challenges in an easy yet flexible way. IAMAP builds on recent advancements in self-supervised learning strategies, which now provide robust feature extractors, often referred to as foundation models. These generalist models can often be reliably used in few-shot or zero-shot scenarios (i.e., with little to no fine-tuning). IAMAP's interface allows users to streamline several key steps in remote sensing image analysis: (i) extracting image features using a wide range of deep learning architectures; (ii) reducing dimensionality with built-in algorithms; (iii) performing clustering on features or their reduced representations; (iv) generating feature similarity maps; and (v) calibrating and validating supervised machine learning models for prediction. By enabling non-AI specialists to leverage the high-quality features provided by recent deep learning approaches without requiring GPU capacity or extensive reference datasets, IAMAP contributes to the democratization of computationally efficient and energy-conscious deep learning methods.

Recent advancements in Generative AI offer promising capabilities for spatial analysis. Despite their potential, the integration of generative AI with established GIS platforms remains underexplored. In this study, we propose a framework for integrating LLMs directly into existing GIS platforms, using QGIS as an example. Our approach leverages the reasoning and programming capabilities of LLMs to autonomously generate spatial analysis workflows and code through an informed agent that has comprehensive documentation of key GIS tools and parameters. The implementation of this framework resulted in the development of a "GIS Copilot" that allows GIS users to interact with QGIS using natural language commands for spatial analysis. The GIS Copilot was evaluated with over 100 spatial analysis tasks with three complexity levels: basic tasks that require one GIS tool and typically involve one data layer to perform simple operations; intermediate tasks involving multi-step processes with multiple tools, guided by user instructions; and advanced tasks which involve multi-step processes that require multiple tools but not guided by user instructions, necessitating the agent to independently decide on and executes the necessary steps. The evaluation reveals that the GIS Copilot demonstrates strong potential in automating foundational GIS operations, with a high success rate in tool selection and code generation for basic and intermediate tasks, while challenges remain in achieving full autonomy for more complex tasks. This study contributes to the emerging vision of Autonomous GIS, providing a pathway for non-experts to engage with geospatial analysis with minimal prior expertise. While full autonomy is yet to be achieved, the GIS Copilot demonstrates significant potential for simplifying GIS workflows and enhancing decision-making processes.

GIS and Drone Technologies are both powerful tools for assisting people in analyzing the world we inhabit. Whether you are someone with GIS skills looking to add drones to your toolbelt, or a drone pilot who wants to level up their deliverable products, you are in the right place. Both these skills require very similar mindsets, such as an attention to detail, focus and accuracy. If you can do one, you can do the other, so why not do both! This course is best for those who have some familiarity with either GIS concepts or drone mapping, or those that are comfortable in learning new software packages.

Prepare your drone flight, analyze the resulting data, and prepare professional reports using QGIS. GIS and Drone Technologies are both powerful tools for assisting people in analyzing the world we inhabit. Whether you are someone with GIS skills looking to add drones to your toolbelt, or a drone pilot who wants to level up their deliverable products, you are in the right place. Both these skills require very similar mindsets, such as an attention to detail, focus and accuracy. If you can do one, you can do the other, so why not do both!

QGIS is a user-friendly open source geographic information system (GIS) that runs on Linux, Unix, Mac OS X, and Windows. The popularity of open source geographic information systems and QGIS in particular has been growing rapidly over the last few years. Learning QGIS Third Edition is a practical, hands-on guide updated for QGIS 2.14 that provides you with clear, step-by-step exercises to help you apply your GIS knowledge to QGIS. Through clear, practical exercises, this book will introduce you to working with QGIS quickly and painlessly. This book takes you from installing and configuring QGIS to handling spatial data to creating great maps.

QGIS is a user-friendly open source geographic information system (GIS) that runs on Linux, Unix, Mac OS X, and Windows. The popularity of open source geographic information systems and QGIS in particular has been growing rapidly over the last few years. Learning QGIS Third Edition is a practical, hands-on guide updated for QGIS 2.14 that provides you with clear, step-by-step exercises to help you apply your GIS knowledge to QGIS. Through clear, practical exercises, this book will introduce you to working with QGIS quickly and painlessly. This book takes you from installing and configuring QGIS to handling spatial data to creating great maps.

More recent works introduced advanced multi-criteria optimization models [12, 21, 27] that can express more accurately several established cartographic principles, but still with the aim of a full automation of the map labeling process. While progress is made by incorporating more comprehensive cartographic rules for label placement, none of the above approaches includes decisions made by human experts - other than setting preferences, parameters, and priorities in the different scoring functions that control a single optimization run of the respective algorithm. A notable exception is the UserHints framework [7], where human interaction was integrated into solving the label number maximization problem in a fixed-position point labeling setting. In that system, two heuristic methods were implemented as labeling algorithms, and hence the evaluation could not assess the deviation from optimal solutions with respect to the objective function. Moreover, the authors did not consider the stability of the labeling under user interaction. Beyond the label placement problem, interactive optimization [22] and human-guided search [16] are of course techniques that are of general interest and more broadly applicable. 2 Popular GIS software like Mapbox 1, ArcGIS Pro 2, or QGIS 3 also provide labeling algorithms. Mapbox allows customized label modifications with data conditions, but no manual selection or drag-and-drop placement. The ArcGIS Pro documentation 4 states "Label positions are generated automatically.

Today I'm writing this post to explain how it's possible to make geographic analysis and answer questions like: which is the richest area in my city? How many people do live in one neighborhood? You can do it combining shape files with an excel spreadsheet, let's understand it together... Then, we're gonna need one shape file and one excel spreadsheet. I'm from Brazil, and we do have a lot of open data from States and Cities.

Are you currently enrolled in either of my Core or Intermediate Spatial Data Analysis Courses? Or perhaps you have prior experience in GIS or tools like R and QGIS? You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis? The next step for you is to gain profIciency in satellite remote sensing data analysis. MY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING DATA WITH OPEN SOURCE TOOLS!