Best AI tools for< Satellite Imagery Technician >

Cybertiks is an AI-powered platform that allows users to monitor and analyze agriculture fields remotely via satellite imagery. The platform offers valuable insights such as nutrients and soil texture, tailored for each field using advanced AI models. Cybertiks specializes in harnessing the power of satellite imagery to provide bespoke solutions for various industries worldwide, with a focus on remote sensing of industrial requirements. The platform integrates accurate AI models trained on thousands of fields to deliver over 80% accuracy in analysis results. Users can create new fields, view historical metrics, monitor field status, and visualize analysis results on a map with high resolution. Cybertiks also offers Sensor Fusion, Certifications, Data Synchronization, Data Integration, and convenient data presentation for clients.

Rapid Editor is an advanced mapping tool that revolutionizes map editing by integrating cutting-edge technology and authoritative geospatial open data. It empowers OpenStreetMap mappers of all levels to make accurate and fresh edits quickly. The tool saves effort by utilizing AI to identify predicted features and provide high-level overviews of unmapped areas globally. Rapid Editor's intuitive interface makes mapping clear and simple, facilitating mapping projects for humanitarian and community groups.

Rapid Editor is an advanced map editing tool that revolutionizes map editing by integrating cutting-edge technology and authoritative geospatial open data. It empowers OpenStreetMap mappers of all levels to make accurate and fresh edits quickly. The tool saves effort by utilizing artificial intelligence to identify features and analyze satellite imagery, providing a high-level overview of unmapped areas globally. Rapid's intuitive user interface simplifies mapping, making it clear and simple for humanitarian and community groups to facilitate mapping projects.

Satlas is an AI-powered platform that provides geospatial data generated by AI models. The platform offers insights into changes in marine infrastructure, renewable energy infrastructure, and tree cover on a monthly basis. Users can explore maps showcasing developments such as wind farms, solar farms, deforestation, and more. Satlas employs advanced AI architectures and training algorithms in computer vision to enhance low-resolution satellite imagery and produce high-resolution images globally. The platform's geospatial datasets are freely available for offline analysis, along with AI models and training labels. Developed by the Allen Institute for AI, Satlas aims to advance computer vision technology for better understanding and monitoring of Earth's changes.

Global Plastic Watch (GPW) is a digital platform that maps the world's plastic pollution in near real-time using a unique combination of satellite imagery and artificial intelligence. It provides a comprehensive view of the global plastic waste crisis, including the location and size of plastic waste sites, the types of plastic waste, and the impact of plastic pollution on the environment and human health.

Deep Planet is a precision viticulture platform powered by AI that focuses on enhancing sustainability in agriculture. It offers solutions for the wine industry, landowners, farmers, and supply chain companies by providing data-driven insights to maximize potential, optimize nutrient application, and support the transition to achieve net zero targets. The platform leverages AI and satellite imagery to empower users with actionable intelligence for better decision-making in vineyard management and soil health.

VIZZIO.AI is an innovative AI-based platform that specializes in ultra-large-scale 3D reconstruction of digital city models worldwide. The platform utilizes smart AI algorithms to create dimensionally-accurate, photorealistic, and semantic 3D digital twins of the planet Earth. Powered by deep learning and satellite imagery, VIZZIO.AI offers timely global coverage for 3D mapping and visualization, enabling cross-platform embedding and planetary-scale city mapping. The platform's AI-based city modeling technology, known as 'EARTH ENGINE,' can identify buildings from above, extrapolate 3D reconstructions, and integrate live video feeds for immersive experiences.

Blackshark.ai is an AI-based platform that generates a real-time accurate semantic photorealistic 3D digital twin of the entire planet. The platform extracts insights about the planet's infrastructure from satellite and aerial imagery using machine learning at a global scale. It enriches missing attributes with AI to provide a photorealistic, geo-typical, or asset-specific digital twin, which can be used for visualization, simulation, mapping, mixed reality environments, and other enterprise solutions. The platform offers features such as Globe Data Input Sources, No Code Data Labeling, Geointelligence at Scale, 3D Semantic Map, and Synthetic Environments.

Neuraspace is an AI/ML solution that focuses on smarter space traffic management, providing scalable tools to protect satellites from collisions and space debris. The platform automates risk assessment and offers maneuver suggestions up to 5 days in advance, streamlining operations for satellite operators, insurance carriers, and regulators. Neuraspace aims to make space operations more efficient and cost-effective, helping small teams handle collision alerts, minimize unnecessary maneuvers, and ensure space safety and profitability.

Satellitor is an AI-powered SEO tool that helps businesses create and manage SEO-optimized blogs. It automates the entire process of content creation, publishing, and ranking, freeing up business owners to focus on other aspects of their business. Satellitor's AI-generated content is of high quality and adheres to Google's best practices, ensuring that your blog ranks well in search results and attracts organic traffic to your website.

fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT) to improve mapping efficiency and accuracy for humanitarian purposes. It uses AI models, specifically computer vision techniques, to detect objects like buildings, roads, waterways, and trees from satellite and UAV imagery. The service allows OSM community members to create and train their own AI models for mapping in their region of interest and ensures models are relevant to local communities. Constant feedback loop with local communities helps eliminate model biases and improve model accuracy.

The Microsoft AI for Earth program hosts geospatial data on Azure that is important to environmental sustainability and Earth science. This repo hosts documentation and demonstration notebooks for all the data that is managed by AI for Earth. It also serves as a "staging ground" for the Planetary Computer Data Catalog.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

Top AI Tools is a comprehensive, community-curated directory that aims to catalog and showcase the most outstanding AI-powered products. This index is not exhaustive, but rather a compilation of our research and contributions from the community.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as a repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which allows benchmarking of various existing and novel AI methods tailored for EO data.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

Geolocation-OSINT is a repository that provides a comprehensive list of resources, tools, and platforms for geolocation challenges and open-source intelligence. It includes a wide range of mapping services, image search tools, AI-powered geolocation estimators, and satellite imagery archives. The repository covers various aspects of geolocation, from finding GPS coordinates to estimating the size of objects in images. Users can access tools for social media monitoring, street-level imagery, and geospatial analysis. Geolocation-OSINT is a valuable resource for individuals interested in geolocation, mapping, and intelligence gathering.

JoliGEN is an integrated framework for training custom generative AI image-to-image models. It implements GAN, Diffusion, and Consistency models for various image translation tasks, including domain and style adaptation with conservation of semantics. The tool is designed for real-world applications such as Controlled Image Generation, Augmented Reality, Dataset Smart Augmentation, and Synthetic to Real transforms. JoliGEN allows for fast and stable training with a REST API server for simplified deployment. It offers a wide range of options and parameters with detailed documentation available for models, dataset formats, and data augmentation.

This repository contains a collection of papers and code for the CVPR 2024 conference. The papers cover a wide range of topics in computer vision, including object detection, image segmentation, image generation, and video analysis. The code provides implementations of the algorithms described in the papers, making it easy for researchers and practitioners to reproduce the results and build upon the work of others. The repository is maintained by a team of researchers at the University of California, Berkeley.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

AI-PhD-S24 is a mono-repo for the PhD course 'AI for Business Research' at CUHK Business School in Spring 2024. The course aims to provide a basic understanding of machine learning and artificial intelligence concepts/methods used in business research, showcase how ML/AI is utilized in business research, and introduce state-of-the-art AI/ML technologies. The course includes scribed lecture notes, class recordings, and covers topics like AI/ML fundamentals, DL, NLP, CV, unsupervised learning, and diffusion models.

This repository is a curated list of awesome public object detection and recognition datasets. It includes a wide range of datasets related to object detection and recognition tasks, such as general detection and recognition datasets, autonomous driving datasets, adverse weather datasets, person detection datasets, anti-UAV datasets, optical aerial imagery datasets, low-light image datasets, infrared image datasets, SAR image datasets, multispectral image datasets, 3D object detection datasets, vehicle-to-everything field datasets, super-resolution field datasets, and face detection and recognition datasets. The repository also provides information on tools for data annotation, data augmentation, and data management related to object detection tasks.

Awesome Large Weather Models (LWMs) is a curated collection of articles and resources related to large weather models used in AI for Earth and AI for Science. It includes information on various cutting-edge weather forecasting models, benchmark datasets, and research papers. The repository serves as a hub for researchers and enthusiasts to explore the latest advancements in weather modeling and forecasting.

The LandingLens Python library contains the LandingLens development library and examples that show how to integrate your app with LandingLens in a variety of scenarios. The library allows users to acquire images from different sources, run inference on computer vision models deployed in LandingLens, and provides examples in Jupyter Notebooks and Python apps for various tasks such as object detection, home automation, satellite image analysis, license plate detection, and streaming video analysis.

Large Language Models (LLM) are used to browse the Wolfram directory and associated URLs to create the category structure and good word embeddings. The goal is to generate enriched prompts for GPT, Wikipedia, Arxiv, Google Scholar, Stack Exchange, or Google search. The focus is on one subdirectory: Probability & Statistics. Documentation is in the project textbook `Projects4.pdf`, which is available in the folder. It is recommended to download the document and browse your local copy with Chrome, Edge, or other viewers. Unlike on GitHub, you will be able to click on all the links and follow the internal navigation features. Look for projects related to NLP and LLM / xLLM. The best starting point is project 7.2.2, which is the core project on this topic, with references to all satellite projects. The project textbook (with solutions to all projects) is the core document needed to participate in the free course (deep tech dive) called **GenAI Fellowship**. For details about the fellowship, follow the link provided. An uncompressed version of `crawl_final_stats.txt.gz` is available on Google drive, which contains all the crawled data needed as input to the Python scripts in the XLLM5 and XLLM6 folders.

Technical satellite security expert trained on space focused cybersecurity frameworks, best practices and process.

UFO/UAP reporting assistant with real-time satellite & flight data analysis.

I help of NASA, guided by official documentation.

Knows the docs and can query official ordinal endpoints—Sat Numbers, Inscription IDs, and more.