Best AI tools for< Satellite Operator >

Neuraspace is an AI/ML solution designed for smarter space traffic management. It automates risk assessment and provides maneuver suggestions up to 5 days before potential collisions, streamlining operations for satellite operators. The platform aims to help satellite companies protect their fleets from space debris, reduce service disruptions, and optimize space operations. Neuraspace offers tools to empower teams to make informed decisions and keep their growing fleets safe with minimal impact on operations.

Cybertiks is an AI-powered platform that specializes in harnessing the power of satellite imagery to provide valuable insights for agriculture fields worldwide. By integrating advanced AI models trained on thousands of fields, Cybertiks offers bespoke solutions for remote sensing of industrial requirements. Users can monitor field metrics, historical insights, and field status changes, with results delivered every 7 days. The platform also integrates various sources of information, provides certifications, synchronizes data, and offers data integration for a comprehensive and strategic vision.

Rapid Editor Mastodon is a versatile online platform that offers a wide range of editing tools for various types of content. Users can easily edit text, images, videos, and more in a user-friendly interface. The platform is designed to streamline the editing process and enhance productivity. With a focus on speed and efficiency, Rapid Editor Mastodon caters to professionals and individuals looking to create high-quality content with ease.

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 map features and provides a high-level overview of unmapped areas using AI-analyzed satellite imagery. Rapid aims to help map the world by displaying open map data and machine learning detections, making mapping clear and simple for humanitarian and community groups.

Satlas is an AI-powered platform that provides geospatial data generated by AI models. The platform showcases how our planet is changing by revealing insights into marine infrastructure, renewable energy infrastructure, and tree cover. Satlas employs state-of-the-art AI architectures and training algorithms in computer vision to enhance low-resolution satellite imagery and produce high-resolution images on a global scale. The AI-generated geospatial datasets are freely available for offline analysis, along with AI models and training labels. The platform is developed and maintained by PRIOR and colleagues at the Allen Institute for AI, aiming to advance computer vision and create AI systems that understand and reason about the world.

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.

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.

Blackshark.ai is an AI-based platform that generates 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 provides a photorealistic, geo-typical, or asset-specific digital twin for visualization, simulation, mapping, mixed reality environments, and other enterprise solutions. The platform offers features such as 3D Semantic Map, Synthetic Environments, ORCA™HUNTR for object identification, SYNTH3D for 3D replica of the planet's surface, Digital Airports, Synthetic Training Data, Semantic 3D City Models, and Geospatial Analytics.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Bosquet is a tool designed for LLMOps in large language model-based applications. It simplifies building AI applications by managing LLM and tool services, integrating with Selmer templating library for prompt templating, enabling prompt chaining and composition with Pathom graph processing, defining agents and tools for external API interactions, handling LLM memory, and providing features like call response caching. The tool aims to streamline the development process for AI applications that require complex prompt templates, memory management, and interaction with external systems.

Harbor is a containerized LLM toolkit that simplifies the initial configuration of various LLM-related projects by providing a CLI and pre-configured Docker Compose setup. It serves as a base for managing local LLM stack, offering convenience utilities for tasks like model management, configuration, and service debugging. Users can access service CLIs via Docker without installation, benefit from pre-configured services that work together, share and reuse host cache, and co-locate service configs. Additionally, users can eject from Harbor to run services without it.

WeatherGFT is a physics-AI hybrid model designed to generalize weather forecasts to finer-grained temporal scales beyond the training dataset. It incorporates physical partial differential equations (PDEs) into neural networks to simulate fine-grained physical evolution and correct biases. The model achieves state-of-the-art performance in forecasting tasks at different time scales, from nowcasting to medium-range forecasts, by utilizing a lead time-aware training framework and a carefully designed PDE kernel. WeatherGFT bridges the gap between nowcast and medium-range forecast by extending forecasting abilities to predict accurately at a 30-minute time scale.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

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**

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.

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.

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.