We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis. FLAIR contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion individually labeled pixels for precise land-cover classification.

The integration of advanced Natural Language Processing (NLP) methodologies and Large Language Models (LLMs) has significantly enhanced the extraction and analysis of geospatial data from multilingual texts, impacting sectors such as national and international security. This paper presents a comprehensive evaluation of leading NLP models -- SpaCy, XLM-RoBERTa, mLUKE, GeoLM -- and LLMs, specifically OpenAI's GPT 3.5 and GPT 4, within the context of multilingual geo-entity detection. Utilizing datasets from Telegram channels in English, Russian, and Arabic, we examine the performance of these models through metrics such as accuracy, precision, recall, and F1 scores, to assess their effectiveness in accurately identifying geospatial references. The analysis exposes each model's distinct advantages and challenges, underscoring the complexities involved in achieving precise geo-entity identification across varied linguistic landscapes. The conclusions drawn from this experiment aim to direct the enhancement and creation of more advanced and inclusive NLP tools, thus advancing the field of geospatial analysis and its application to global security.

Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims), a no-code web tool that allows users to perform clustering and similarity search over defined regions of interest using Google Earth Engine as a backend. Sims is designed to complement existing modeling tools by focusing on feature exploration rather than model creation. We demonstrate the utility of Sims through a case study analyzing simulated maize yield data in Rwanda, where we evaluate how different combinations of soil, weather, and agronomic features affect the clustering of yield response zones. Sims is open source and available at https://github.com/microsoft/Sims

We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis. FLAIR contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion individually labeled pixels for precise land-cover classification. FLAIR thus combines data with varying spatial, spectral, and temporal resolutions across over 817 km² of acquisitions representing the full landscape diversity of France. This diversity makes FLAIR a valuable resource for the development and evaluation of novel methods for large-scale land-cover semantic segmentation and raises significant challenges in terms of computer vision, data fusion, and geospatial analysis. We also provide powerful uni- and multi-sensor baseline models that can be employed to assess algorithm's performance and for downstream applications.

According to its documentation, TorchGeo is a "PyTorch domain library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data". Make it easier for practitioners to use Deep Learning models on geospatial data. And why is that a good deal? In a last years' presentation from Dan Morris (former principal scientist at Microsoft's AI for Earth program) to the IEEE-GRSS (Geoscience and Remote Sensing Society), he highlighted some challenges related to geospatial analysis (link to the presentation is here): On the top of that, people working with Artificial Intelligence for geospatial analysis haver an extra layer of complexity, because most frameworks are developed for RGB pictures and don't take into account the specificities of geospatial data: So, at the present, it is really challenging for someone to apply deep learning models to geospatial tasks without having knowledge on these diverse subjects. In this context, the TorchGeo library has been launched on November 2021 to address some of these challenges.

Artificial intelligence has infiltrated practically every civilian industry imaginable. It has changed the way people and businesses work, and it is now swiftly becoming a necessary component of modern combat. One of the criteria that determines how powerful a country is the strength of its army. When compared to other parts, investment in this industry is the largest in some of the most developed countries. A significant portion of this investment is dedicated to rigorous research and development in current technologies, such as artificial intelligence (AI) in military applications.

Artificial intelligence has penetrated almost all civilian industries that one can think of. It has transformed the way individuals and businesses work, and now it is quickly making its way in becoming a critical part of modern warfare. The strength of its army is one of the factors indicating how powerful the country is. In some of the most developed nations, investment in this sector is the highest as compared to other sectors. A major part of this investment goes towards rigorous research and development in modern technology such as AI in military applications.

Last month, we asked readers and our social media followers what questions they had for our machine learning engineers Lewis Fishgold, James McClain, and Rob Emanuele. We received a variety of questions that covered different topics within the world of geospatial machine learning. We covered questions about getting started with machine learning in Part 1. This post will cover the future of machine learning! None of these three things are unique to the geospatial domain.

When it comes to the relationship between business development and technological innovation, we can generally separate two schools of thought. There are those who believe that technological progress is what propels businesses forward. And on the other hand, there are those who are certain that business investments are what makes innovations like contemporary geospatial AI possible. As with most opposing opinions – the truth is somewhere in between. Or, rather, the relations between cutting-edge tech and emerging business sectors are a never-ending circle; with business financing the research and development that enables the appearance of new tech, which in turn leads to new business opportunities and sectors.

Learning Geospatial Analysis with Python: Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7, 3rd Edition. Learn the core concepts of geospatial data analysis for building actionable and insightful GIS applications You Can Buy This Book "Learning Geospatial Analysis with Python: Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7, 3rd Edition Kindle Edition" for only $35.99 at Amazon Key Features Create GIS solutions using the new features introduced in Python 3.7 Explore a range of GIS tools and libraries such as PostGIS, QGIS, and PROJ Learn to automate geospatial analysis workflows using Python and Jupyter Book Description Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data.