Jon Booth’s Post

Upcoming #webinar alert: Redefining Image Analysis with ENVI 6.0, IDL 9.0, and the ENVI Ecosystem. As the way people interact with imagery has changed, so has NV5 Geospatial! Join this webinar to learn how their new product releases will transform the way that you, and your organization, work with imagery. Attend this webinar to learn: * New image and #SAR processing #workflows that make science approachable * How users can improve productivity with new workflows and developer tools * A new playground for data scientists powered by #IDL Notebooks * How experts and non-experts can easily collaborate to solve #geospatial problems * How you can use AI to super-charge video and image analysis There will be Q&A at the end of the webinar to answer your questions. Date: Tuesday, 30th November 2023 Time: 3:00 PM GMT (for the EMEA region) Click here to register: https://bit.ly/3uhlwIj #envi #remotesensing #geospatialtechnology #datascientists

Multi Modal Knowledge Graph #Embeddings - Key Trends As the industry moves towards building complex #LLM products, a few trends are becoming quite important. These trends are very often driven by the need to build higher ROI #GenAI industry products. Specifically, the trends include (but not limited to):   a.     Context strengthening through better information retrieval & information modeling – specifically, for this post, the use of Knowledge Graphs, semantic web & approaches like the Graph RAG have shown promise.   b.     Use of real-world inputs, such as multi modal data – a recent design example, I’ve been involved with used drone sent images, sensor data & various forms of metered data, to feed into multiple knowledge graph RAGs. c.     Of course, structuring approaches such as advanced RAG, DSPy, agentic flows & others, along with building compound AI systems, through chained inference steps or reasoned function calling. As Graph RAG like approaches arise, it is important to understand the unique needs of Multi Modal Knowledge Graphs. Some base “thought” issues that arise, include: 1.     What are the models of using multi modal data in knowledge graphs? 2.     How do you populate knowledge graphs with multi-modal data & relationships? 3.     What is the nature of the data (especially embeddings) that are maintained in these multi-modal knowledge graphs? It is the last question, on which a fair amount of new research is being done and is critical for defining successful hybrid search approaches when Multimodal LLM-based Graph-RAGs are built. The intent of the blog is to point to key shifting trends in generating Knowledge Graph embeddings for multi modal data, using the backdrop of 2 recent seminal papers. For more details on this line of thinking, see https://lnkd.in/gfPmzBgv Note:: for some early efforts at building multi modal KG RAG pipelines, using LlamaIndex and Neo4j – you can check out - https://lnkd.in/g9xnCSTu

"Exciting developments in the world of computer vision and aerial data analysis! A new paper offers a comprehensive review of the latest technologies and tasks, including object detection and tracking, change detection, and scene-level analysis. The research delves into architectural nuances, evaluation metrics, and practical applications across various domains, shedding light on future research directions. Stay updated with the latest in AI and computer vision. #AI #ComputerVision #AerialData #ResearchReview"

Exploring the strengths of ORB, SIFT, and FREAK for image alignment. Dive into my latest Medium post for insights into these algorithms and how they can improve your workflows. #ImageProcessing #ComputerVision #DocumentProcessing #MachineLearning #DataScience #AI #AlgorithmAnalysis #TechInnovation

Geo-Zoning Through Driving Distance Using K-Medoids Algorithm https://bit.ly/3S5oRnv Geo-Zoning is a method used to partition a geographical area into distinct zones or regions, with a set of rules or guidelines governing activities and land use within its boundaries using driving distance or driving time. This concept is widely used in urban planning, land use management, representatives to locate customers seamlessly, and various other fields. The K-Medoid algorithm is a partition technique of clustering that clusters into K groups around medoids, which are data points representative of clusters; unlike the k-means algorithm, which calculates the mean for each cluster to minimize the variance, the k-Medoids algorithm selects actual data points to represent the clusters in small equidistant K groups. via DZone AI/ML Zone https://bit.ly/41qvfZp January 02, 2024 at 11:15AM

In an era of rapid technological expansion, the field of Geospatial Intelligence (GEOINT) is experiencing a particularly profound transformation. At the forefront of this evolution, Markon is driving change by integrating agile management approaches, artificial intelligence, machine learning, and digital engineering to redefine intelligence capabilities. Our approach enhances operational efficiency, precision, and strategic alignment in GEOINT operations. Dive into the trends and methodologies that are revolutionizing this dynamic field in our recent blog >> https://hubs.li/Q02JB8Fp0 #GEOINT #DedicatedToWhatMatters

ai meets spatial data, this is to awesome! GeoGPT: An assistant for understanding and processing geospatial tasks. Keywords: Geospatial semantic understanding AutoGPT GeoAI Foundation model

AI Assistants in #ArcGIS are here. Esri has implemented GenAI across the platform making smart mapping automated and truly intelligent. Check out this example from the Esri User Conference. https://lnkd.in/gUAfyVs6 #EsriTelecom #Telecommunications

#Lets_talk_GeoAI I'm starting a monthly program for daily video publication focusing on GeoAI (#GeoAI), which can provide a comprehensive overview and in-depth knowledge to viewers interested in geospatial technologies and services. #day5: GeoAI leverages various #data sources to #analyze and interpret geographical and spatial data. The integration of #AI with geospatial data has transformed how we understand and interact with the #physical world. Please watch this video: https://lnkd.in/ecXpkfiC

Let's Talk AI and GIS! AI isn't just a buzzword; it's a transformative tool that revolutionizes how we approach FME and ArcGIS. Here are a few examples of how AI can enhance data management: ➡️ Assistance in Material Development ➡️ Data Analysis and Pattern Detection ➡️ Forecasting At Consortech, we don't just embrace innovation; we also guide our clients every step of the way. Our team of experts is dedicated to translating the latest advancements in AI and FME into tangible benefits for your organization. Ready to unlock the full potential of AI? Let's embark on this journey together! #EsriGIS #EsriPartners #FME #SafeSoftware