#AI-Geospatial

[iammannyj]

Harnessing the Power of GIS Data for SEO: Transforming Digital Marketing Strategy

Harnessing the Power of GIS Data for SEO: Transforming Digital Marketing Strategy

Having recently completed comprehensive training in Geospatial Data Collection, Analysis & Interpretation using GIS Technology, I’ve discovered fascinating intersections between my established background in SEO and digital marketing and the powerful capabilities of geospatial analysis. This educational journey, covering everything from fundamental GIS concepts to advanced geospatial analysis and…

[datastring]

Geospatial Solutions Market Set to Hit $2401.1 Billion by 2035

The global market for Geospatial Solutions is expected to experience significant growth, with industry revenue projected to rise from $488.0 billion in 2023 to $2401.1 billion by 2035. This reflects a compound annual growth rate (CAGR) of 14.2% from 2023 to 2035.

Detailed Analysis - https://datastringconsulting.com/industry-analysis/geospatial-solutions-market-research-report

Geospatial solutions are essential across a wide range of applications, including precision agriculture, urban planning, logistics and transportation, as well as defense and security. The market's growth is primarily driven by the increasing adoption of geospatial technologies in key sectors such as agriculture, urban development, and defense.

Competitive Landscape and Market Leadership

The Geospatial Solutions market is highly competitive, with several key players driving innovation and market penetration. Leading companies in the market include:

Esri

Hexagon Geospatial

Trimble

Topcon

HERE Technologies

Fugro

TomTom

Geo-Insights

Blue Marble Geographics

SuperMap

Mapbox

Cyient

These companies are at the forefront of advancing geospatial technologies, such as artificial intelligence (AI), machine learning, and real-time data analytics, which are helping to meet the growing demand for smarter and more efficient solutions across industries.

Key Growth Drivers and Market Opportunities

The growth of the Geospatial Solutions market is fueled by several factors:

Precision Agriculture: The growing need for efficient land use and sustainable farming practices is driving the demand for geospatial solutions in agriculture, enabling better crop management and resource allocation.

Urbanization and Smart City Initiatives: The increasing push for smart city development and urban planning requires geospatial technologies to manage infrastructure, transportation, and urban environments more efficiently.

Defense and Security: Geospatial solutions are playing a crucial role in defense and security applications, including surveillance, reconnaissance, and situational awareness.

Integration of AI and Machine Learning: The application of AI and machine learning in geospatial data analysis is enhancing the capabilities of these solutions, enabling real-time insights and decision-making.

Demand for Real-time Location Data: The growing reliance on real-time data in various sectors, including logistics, transportation, and emergency management, is driving the adoption of geospatial technologies.

Regional Dynamics and Supply Chain Evolution

North America and Asia-Pacific are the dominant regions in the Geospatial Solutions market. Both regions benefit from strong technological infrastructure, high levels of investment, and significant demand from key industries. However, emerging markets in India, Brazil, and South Africa are becoming increasingly important for market growth, driven by rapid urbanization, industrialization, and the adoption of new technologies.

Despite challenges such as high initial investments, data privacy concerns, and integration complexities, the geospatial solutions market’s supply chain—from data providers and software developers to system integrators and service providers—is expected to evolve to meet these challenges. Companies are also focusing on strategic advancements in developing regions to diversify revenue streams and expand their total addressable market (TAM).

About DataString Consulting

DataString Consulting is a leading provider of market research and business intelligence solutions, offering a comprehensive range of services for both B2C and B2B markets. With over 30 years of combined industry experience, DataString specializes in delivering actionable insights that support strategic decision-making.

The company’s expertise spans multiple industries, providing tailored research services in strategy consulting, opportunity assessment, competitive intelligence, and market forecasting. DataString Consulting helps businesses navigate complex markets and capitalize on emerging trends to achieve long-term success.

[mappingtomorrow]

This in-depth exploration of Geographic Information Systems with Artificial Intelligence (GeoAI) shows how this revolutionary technology is transforming spatial analysis across industries. From urban planning and disaster management to precision agriculture and retail intelligence, GeoAI combines machine learning, deep learning, computer vision, and spatial data to solve complex geographic problems with unprecedented accuracy and efficiency. This post covers the three core pillars of GeoAI: spatial data infrastructure, AI algorithms, and computational resources, while tracing its evolution from manual mapping to today's autonomous systems. Readers will discover key technologies driving innovation, including computer vision for earth observation, NLP for geographic information retrieval, and reinforcement learning for routing optimization. I request you to have a read and engage with the post, mentioning what do you think where will the Geospatial Industry be in the upcoming years until 2030. Do share it within your professional networks and spread the word of GeoAI and how it is transforming the Geospatial industry.

[michaelgabrill]

Entrepreneurs Challenge Winner PRISM is Using AI to Enable Insights from Geospatial Data

NASA sponsored Entrepreneurs Challenge events in 2020, 2021, and 2023 to invite small business start-ups to showcase innovative ideas and technologies with the potential to advance the agency’s science goals. To potentially leverage external funding sources for the development of innovative technologies of interest to NASA, SMD involved the venture capital community in Entrepreneurs Challenge […] from NASA https://ift.tt/tBIjbwc

[govindhtech]

TerraTorch 1.0: Streamlining Geospatial AI For Everyone

TerraTorch 1.0 simplifies geospatial AI. Geographic model construction and comparison are easier with AI Alliance affiliate project TT 1.0.

Everyone is excited to unveil TerraTorch (TT) 1.0, a major geographic AI improvement. TerraTorch was intended for geospatial AI researchers who wish to enhance foundation models and exploit their data. Integrating and standardising foundation models as reusable backbones improves this process and allows smooth integration with cutting-edge decoders and heads.

They are part of the optimisation aim since different tasks (regression, classification, segmentation, object identification, etc.) need different heads and decoders perform differently on different datasets, backbones, and head combinations. TerraTorch, designed for geospatial, meteorological, climatic, and Earth observation applications, has become a powerful foundation model inference and fine-tuning framework.

TerraTorch recently introduced "necks" intermediary layers that adapt between incompatible model components, increasing architectural design flexibility. Because all neural network components are modularised and available through a TerraTorch execution configuration YAML file and the Python API, the framework improves repeatability, integration, and user error by minimising glue code.

Finally, this configuration-based technique perfectly interfaces with the TT Iterate plugin for NAS and HPO. Users may optimise backbones, decoders, and heads and fine-tune hyperparameters for model performance with this tool. TerraTorch is needed to benchmark foundation models according to GeoBench, which ensures full geospatial AI model evaluation and comparison.

TerraTorch adapts geospatial foundation models to EO applications quickly. Model validation and comparison are accelerated by integrated metrics. Researchers utilising TerraTorch to fine-tune Prithvi-EO-2.0 for flood, wildfire, and burn intensity tracking, landslip diagnosis, and crop segmentation are receiving positive feedback from the community.

TT 1.0 provides fingertip access to a choice of foundation models with opinionated performance optimisations (default settings based on best practices) that may be changed to help clients get started quickly while preserving flexibility for fine-tuning. Quick testing and deployment of cutting-edge geospatial AI models is made possible.

TerraTorch joins AI Alliance

TerraTorch's AI Alliance Affiliate Project status is a highlight of this edition. TerraTorch's integration within a larger AI research and practice community assures alignment with open AI development best practices and supports geographic machine learning innovation.

Iterate: HPO/NAS geospatial AI

The Iterate plugin for NAS and HPO is essential to TT 1.0. Iterate simplifies model selection and optimisation by automating architecture and hyperparameter selection on Optuna and optionally using MLFlow and Ray Tune. Iterate also benchmarks foundation models using GeoBench, ensuring a full evaluation of geospatial AI models against real datasets.

A complete foundation model and data module set

TT 1.0 simplifies geographic AI modelling with its large foundation model and data module library. Pre-training, fine-tuning, and inference are possible with TT 1.0. A customisable YAML configuration file or PyTorch Lightning-compatible programs can configure it.

Deep TorchGeo, CLAY, and IBM open-source model integration

TT 1.0 simply integrates with TorchGeo to access all models and data modules in the ecosystem. TerraTorch's intrinsic support for many open-source geospatial foundation models, including Granite, Prithvi, Clay, SatMAE, Satlas, DeCur, and DOFA, strengthens its position as a premier geospatial AI framework.

Image classification, multivariate multiple regression, and semantic segmentation with configurable heads are possible with TT 1.0.

It also supports ViT-Adapter and LoRA parameter efficient fine-tuning (PEFT). TT 1.1 supports VPT.

In addition, TT 1.0 lets users pick any Timm or SMP model for a job.

TerraTorch powers IBM's Geospatial Studio's scalable AI inference and fine-tuning

TerraTorch supports scalable AI inference and geospatial application fine-tuning in IBM's Geospatial Studio. The platform's basis allows data preprocessing, model training, and real-time satellite imagery analysis.

TerraTorch uses community-driven solutions to decrease vendor lock-in, promote transparency, and spur creativity via open-source Geospatial Studio. Geospatial Studio can instantly leverage the newest foundation model and AI best practices because it is not limited to IBM's stack.

Terratorch/vLLM models

Dynamic batching, smart memory management, and Paged Attention make vLLM an optimised open-source LLM inference library for latency-free streaming.

Using TerraTorch to produce and consume geographical data extends vLLM beyond text to multi-modal AI. Adding IBM Research's Prithvi-EO-2.0, vLLM's first non-text input/output model, was a milestone. Current work intends to accelerate this trend by adding non-text post-processing stages to vLLM, which can use TerraTorch for computer vision tasks in geographic models. This lays the framework for multimodal vLLM.

What's next?

The release of TT 1.0 is only the beginning. To be at the forefront of geographic AI innovation, TerraTorch constantly improves, supports new models, datasets, tasks (such object identification), and benchmarking standards.

The GIS and AI communities are invited to examine TT 1.0, influence its future, and contribute to its development. Try it now to join this fascinating journey!

[cyberswift-story]

Embracing Trends, the Ghibli Way!

At CyberSWIFT, we don’t just follow trends—we bring them to life! Inspired by the charm of Ghibli-style storytelling, we blend AI, GIS, and innovation to craft future-ready solutions. 🚀

By embracing the latest advancements, we help businesses stay ahead in the AI revolution, unlocking new possibilities with smart, scalable technology.

🔹 Innovation meets imagination—are you ready? Let’s create something extraordinary together!

[forlinx]

Advantages of FET3588J-C SoM in UAV Hyperspectral Imager Application

Product Background

With the rapid development and widespread application of drone technology, drone-mounted hyperspectral imaging systems that integrate drone platforms with high-precision spectral imaging technology are gradually gaining attention. The drone-mounted hyperspectral imaging system can measure spectral information of plants, water bodies, soil, etc., in real-time and generate spectral images. By analyzing these images, it is possible to establish relationships with the physicochemical properties of plants and use the data for research on plant classification and growth conditions. Additionally, it has applications in geological and mineral resource exploration, forest pest and disease monitoring, meteorological studies, and other industry fields.

Product Features

Hyperspectral resolution: Capable of capturing hundreds of continuous and narrow spectral bands, enabling detailed spectral characterization of surface materials.

High spatial resolution: Combined with the flexibility and stability of the drone platform, this enables high-resolution imaging of ground targets.

Intelligent data processing: Integrates advanced image processing algorithms and spectral analysis software, supporting rapid processing and interpretation of data.

Portability and ease of use: The equipment is lightweight and portable, making it easy to mount on drone platforms, while the user interface is user-friendly, simplifying operation for users.

Broader application scenarios: Suitable for environmental monitoring, agricultural surveys, geological exploration and other fields, meeting the needs of different users.

Product Requirements:

1. Compact carrier board design: Considering the weight and space constraints of the drone, the accompanying imaging device needs to be designed as compactly as possible. The carrier board design should be compact, effectively utilizing every inch of space while ensuring that all key components can be securely installed.

The size of the SoM should also be strictly controlled to fit the compact carrier board layout, without sacrificing its performance.

2. High compression ratio image compression algorithm: The product should incorporate a proprietary high compression ratio image compression algorithm to optimize the storage and transmission efficiency of image data. The algorithm needs to fully leverage the computational power of the SoM to ensure that excessive performance loss does not occur during image compression. The compression algorithm should minimize file size while maintaining image quality to accommodate the limited data transmission bandwidth and storage space of drones.

3. High-quality product stability: Given the complexity of the product application environment, such as extreme weather and mechanical vibrations, the requirements for product quality and stability are extremely high. All components and materials shall be of industrial grade or higher quality to ensure proper operation in a variety of harsh environments. Products shall be subjected to rigorous tests for shock, impact and weather resistance to verify their reliability in complex application environments.

Based on the product characteristics, Forlinx Embedded recommends using the FET3588J-C system on module(SoM) as the hardware design solution for the product.

Solution Features:

1. Strong performance support

High-performance processor: FET3588-C is based on Rockchip's new flagship RK3588 processor, which adopts an 8nm manufacturing process and integrates a quad-core Cortex-A76+quad-core Cortex-A55 architecture with a clock speed up to 2.4GHz, ensuring strong data processing capabilities for drones during hyperspectral imaging.

Powerful computing power: The built-in NPU provides 6 TOPS computing power, which makes it possible for artificial intelligence to be applied in UAV hyperspectral imaging, such as automatic target recognition, real-time analysis, etc.

2. Excellent image processing capabilities

Next-generation ISP 3.0: The FET3588-C introduces a 48-megapixel ISP 3.0, which supports various image optimization functions such as lens shading correction and 2D/3D noise reduction. This is crucial for enhancing the quality and clarity of hyperspectral imaging.

3. Highly integrated and scalable

Abundant high-speed data communication interfaces: FET3588-C is equipped with high-speed data communication interfaces, which can ensure the rapid transmission and processing of hyperspectral imaging data and improve the operation efficiency.

4. Wide application adaptability

Temperature range upgrade: The temperature range of the commercial-grade FET3588-C SoM is increased from 0 ℃ ~ + 80 ℃ to -20 ℃ ~ + 85 ℃, enabling the UAV to perform hyperspectral imaging operations in a wider range of environmental conditions.

Support for multiple operating systems: Adapt to multiple operating systems, provide flexibility for different users and development environments, and facilitate the integration and development of hyperspectral imaging systems.

In short, the FET3588-C SoM offers strong performance, great image processing, flexible display setup, high integration and scalability, and wide adaptability for use in unmanned aerial vehicle hyperspectral imaging equipment. These advantages make FET3588-C an ideal choice for UAV hyperspectral imaging.

Originally published at www.forlinx.net.

[newspatron]

Airtel & Google Cloud Soar: 10 Ways Businesses Benefit

Have you explored the Airtel Google Cloud partnership yet? Share your thoughts and questions below!

A New Chapter for Indian Businesses: The Airtel and Google Cloud Alliance Airtel #GoogleCloud #CloudComputing #AI #India #Business #Technology #Partnership #Innovation #Growth #GeospatialAnalytics #VoiceAnalytics #MarketingTechnology #IoT #DigitalTransformationCloud Computing: The Engine Powering India’s Business BoomAirtel: Empowering Indian BusinessesGoogle Cloud: Global Innovation, Local…

[airwavesdotblog]

High Water Ahead: The New Normal of American Flood Risks

According to a map created by the National Oceanic and Atmospheric Administration (NOAA) that highlights ‘hazard zones’ in the U.S. for various flooding risks, including rising sea levels and tsunamis. Here’s a summary and analysis: Summary: The NOAA map identifies areas at risk of flooding from storm surges, tsunamis, high tide flooding, and sea level rise. Red areas on the map indicate more…

[thecatamaranlad]

What are your strong opinions on science?

hi thanks for asking can we be new best friends <3

FIRST OFF, that the public desperately needs a better understanding of *the scientific method* and how it deals with uncertainty, to be able to deal with things like COVID and climate change (extreme events are, by nature, random and extreme). science was changing constantly and discovering new things, and because people didn't understand that's how it's supposed to work they went "welp scientists don't know jack" (and were able to be manipulated into believing that). better public education of the scientific method would do quite a lot to prevent that sort of misinformation takeover, or the kind that's used by tobacco (now vapes lol) and oil companies. the public has somewhat of an excuse; reporters don't. (or they wouldn't if they weren't underpaid and worked to death. i digress.)

second off... hmm, actually, i've changed my opinion on the IPCC's RCP 8.5 to a more nuanced take (it's an extreme carbon emissions pathway that *does* perform well in the next few decades on most models even though it's the wrong pathway now that we've reduced emissions/failed to raise them as much as we feared in the 90s, but honestly the fact that it still performs well is terrifying for OTHER reasons... namely, we could be missing something *big* in how our models should work). but also i think we need to be a bit more transparent when using it -- it's no longer the "business as usual" climate change scenario.

third off, SCIENTISTS (and businesses) NEED TO STOP P-HACKING AND TAKE A STATS COURSE OH MY GOD. OH MY GOD. See this XCKD comic for an illustration/explanation but essentially p-hacking is when you just keep testing a bunch of things until you find something that passes a "significance" threshold... think of it like rolling different colored sets of dice to get snake eyes and then deciding oh! Blue dice get snake eyes more often than others! Grrrrr. Sound familiar? (Glares daggers at most AI companies. Also a disconcertingly large amount of folks working with geospatial data.) Machine learning is just fancy statistics so you need to like, actually consider statistics when using it.

thank you and more opinions are regularly loaded in the barrel *salutes you*

[darkmaga-returns]

Remember Pokémon Go? You know, that cute game where you And! Battle . . . Pokeballs? . . . to capture . . . .monsters in a gym? . . . or something like that. Anyway, the point is, people went bananas over this augmented reality nonsense eight years ago and of course those crazy conspiracy kooks at places like The Corbett Report had to rain on everyone’s parade by warning them about the app’s shady, intel-connected origins. Well . . . guess who just got proven correct again. (SPOILER: it’s the crazy conspiracy kooks who were just proven correct again.)

Video player not working? Use these links to watch it somewhere else!

WATCH ON:  /  /  /  /  /  or DOWNLOAD THE MP4

SHOW NOTES:

POKÉMON GO – What You Need to Know

Everyone is going crazy over Pokémon Go

Pokémon Go is a viral phenomenon

Pokémon Go – Vaporeon stampede Central Park, NYC

Pokémon Go wikipedia

Pokémon Go Came Out In the US, Let’s Catch ‘Em All

The CIA’s ‘Pokémon Go’ App is Doing What the Patriot Act Can’t

The CIA helped sell a mapping startup to Google. Now they won’t tell us why

Niantic story

Pokémon Go to The Military Industrial Complex

Building a Large Geospatial Model to Achieve Spatial Intelligence

‘Pokémon Go’ Players Are Training AI Models To See The World

The Drone Wars: You Are Not Prepared

BELLINGFEST DAY 1 (Niantic exec questioned on potential military use)

Niantic Exec Comments On Governments Buying Pokémon GO Data

Episode 145 – You Are Being Gamed

Most Disturbing Presentation Ever: Our Tech Nightmare (“Skinner Box”) DICE 2010

Ernest Hancock interviews James Corbett – 2024/11/27

[monetizeme]

"A Niantic executive said that he “could definitely see” governments and militaries purchasing the company’s newly announced AI model for navigating the real world, which would be based on scan data generated by Pokémon Go players, but that if the use case is specific to the military and “adding amplitude to war, then that’s definitely an issue.” 

The comment was made by Brian McClendon, Niantic’s Senior Vice President of Engineering and formerly the co-creator of Google Earth, Street View, and Google Maps, at the investigative journalism group Bellingcat’s Bellingfest event on November 14. McClendon was giving a talk titled “Coordinates of tomorrow: Why spatial computing needs a new map,” which covered his history in the industry, his work at Google and Niantic, and some details on Niantic’s Large Geospatial Model, or LGM, that the company announced two days earlier. 

During a questions and answers portion after his talk, Bellingcat’s open source analyst and ex-British Army officer Nick Waters said that LGMs would be “unbelievably useful” to the military and asked if McClendon could see governments and militaries purchase LGMs from Niantic. 

“I could definitely see it,” McClendon said. “I think the question is would there be anything that they would do with it that would be outside of what a consumer or a Bellingcat want to do with it. If the use case is identical then that seems completely fine. If the use case is specific in military and adding amplitude to war then that’s obviously an issue.”

McClendon did not rule out selling Niantic’s data or LGM to governments and militaries."

[michaelgabrill]

Geospatial AI Foundation Model Team Receives NASA Marshall Group Achievement Award

NASA’s science efforts aim to empower scientists with the tools to perform research into our planet and universe. To this end, a collaborative effort between NASA and IBM created an AI geospatial foundation model, which was released as an open-source application in 2024.  Trained on vast amounts of NASA Earth science data, the foundation model […] from NASA https://ift.tt/IgcCVYr

[sentivium]

Niantic is building a ‘geospatial’ AI model based on Pokémon Go player data

Scans of the world from Pokemon Go and Ingress are the backbone of Niantic’s AI model, which aims to navigate the world like ChatGPT spits out text.