Best AI tools for< Satellite Operator >

Neuraspace is an AI/ML solution designed to streamline space traffic management operations by automating risk assessment and providing maneuver suggestions up to 5 days before potential collisions. The platform aims to help satellite operators, insurance carriers, regulators, and partners in the space industry to handle collision alerts efficiently, minimize unnecessary maneuvers, and optimize space operations. Neuraspace offers scalable, reliable, and cost-effective AI tools to protect satellite fleets, avoid service disruptions, collisions, and liability, ultimately ensuring space safety and profitability.

Cybertiks is an AI-powered application that offers remote monitoring and analysis of agriculture fields using satellite imagery. The application provides valuable insights such as soil nutrients and texture, tailored AI models for accurate metrics, historical data analysis, and visualization of results on a map. Cybertiks integrates various sources of information, offers tailored solutions for clients, and uses quantum-enabled artificial intelligence for material detection and classification. The application is designed to meet the needs of diverse industries worldwide by harnessing the power of satellite imagery and AI technology.

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 to make accurate and fresh edits quickly at all levels. The tool saves effort by tapping into open data and using artificial intelligence to identify map features. It provides AI-analyzed satellite imagery for a high-level overview of unmapped areas globally, and displays open map data and machine learning detections to facilitate 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.

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 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.

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.

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.

hongbomiao.com is a personal research and development (R&D) lab that facilitates the sharing of knowledge. The repository covers a wide range of topics including web development, mobile development, desktop applications, API servers, cloud native technologies, data processing, machine learning, computer vision, embedded systems, simulation, database management, data cleaning, data orchestration, testing, ops, authentication, authorization, security, system tools, reverse engineering, Ethereum, hardware, network, guidelines, design, bots, and more. It provides detailed information on various tools, frameworks, libraries, and platforms used in these domains.

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.

The TME-AIX repository is a collaborative workspace dedicated to exploring Telco Media Entertainment use-cases using open source AI capabilities and datasets. It focuses on projects like Revenue Assurance, Service Assurance Predictions, 5G Network Fault Predictions, Sustainability, SecOps-AI, SmartGrid, IoT Security, Customer Relation Management, Anomaly Detection, Starlink Quality Predictions, and NoC AI Augmentation for OSS.

Telco-AIX is a collaborative experimental workspace dedicated to exploring data-driven decision-making use-cases using open source AI capabilities and open datasets. The repository focuses on projects related to revenue assurance, fraud management, service assurance, latency predictions, 5G network operations, sustainability, energy efficiency, SecOps-AI for networking, AI-powered SmartGrid, IoT perimeter security, anomaly detection, root cause analysis, customer relationship management voice app, Starlink quality of experience predictions, and NoC AI augmentation for OSS.

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**

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.