24 skills found
lumenwrites / NulisMind-mapping software that helps writers collect and organize their knowledge, develop their ideas. Built with React, Redux, Node.js, hosted on Digital Ocean.
oceanmapping / CommunityOcean mapping resources from around the world
pagraf / MagicBathyNetQuick start guide for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification using Remote Sensing imagery.
monocilindro / Awesome HydrospatialA curated list of public available resources for the ocean mapping community
ocean-data-challenges / 2020a SSH Mapping NATL60A challenge on the mapping of satellite altimeter sea surface height data organised by MEOM@IGE, Ocean-Next and CLS.
hydroffice / Hyo2 SoundspeedA package for sound speed profiles
pagraf / Swin BathyUNetQuick start guide for Swin-BathyUNet.
xy1377660586 / Fine Tuning A Pre Trained CNN For First Year Sea Ice And Multi Year Sea Ice Cp Imagery ClassificatioMapping first-year sea ice and multi-year sea ice in the oceans is significant for many applications. For example, ship navigation and weather forecast. Accurate and robust classification methods of multi-year ice and first-year ice are in demand [2]. Hybrid-polarity SAR architecture will be included in future SAR missions such as the Canadian RADARSAT Constellation Mission (RCM). These sensors will enable the use of compact polarimetry (CP) data in wide swath imagery [1]. Convolutional neural networks (CNNs) are becoming increasingly popular in many research communities due to availability of large image datasets and high-performance computing systems. As Convolutional networks (ConvNets) have achieved great success on many image classification tasks, I pursue this method for the classification of image patches from compact polarimety (CP) imagery into first-year ice and multi-year ice is applicable. In this course project, my work is kind of like the first practice of the CP imagery classification by fine-tuning a pre-trained convolutional neural network (CNN). Specifically, fine-tuning the last fully-connected layer of a pre-trained convolutional networks, I extract patches from simulated CP images as my dataset, the classification accuracy of the test set achieved 91.3% by fine-tuning a pre-trained CNN, compared to 49.4% classification accuracy by training from scratch.
ocean-data-challenges / 2021a SSH Mapping OSEA challenge on the mapping of real satellite altimeter sea surface height data organised by MEOM@IGE, Ocean-Next and CLS.
SAED2906 / WorldMachinaA physically accurate planet generation and visualization tool built with Python and OpenGL. Create detailed planetary terrain using tectonic plate simulation, height-based displacement mapping, and realistic ocean rendering.
smartin98 / NeurOSTNeural Ocean Surface Topography (NeurOST). High-resolution global sea surface height and surface current mapping through deep learning synthesis of multimodal satellite observations.
hydroffice / Python BasicsProgramming Basics with Python
hydroffice / Ocean Data ScienceIntroduction to Ocean Data Science
MBAdv / Multibeam ToolsPython-based tools for assessing Kongsberg multibeam echosounder performance
hydroffice / Hyo2 QcQuality Control Tools for Ocean Mapping
richiecarmichael / Esri Ocean CurrentsMapping the world's seasonal ocean currents
pagraf / Seabed NetQuick start guide for Seabed-Net
RheoDesign / AAVS BeijingTITLE: SU(PE)RREAL Director: Li-Qun Zhao SuperReal is about the manipulation of the mass information in the Big Data Era. Due to the development of multi-media technologies, everyone has submerged in the data ocean. Data could be generated by anything surround us. Instead of generating forms and effects, the key of SuperReal is, how we can parameterize the information mapping, regardless visible or invisible, with visual communication. Various multi-media tools will be used in data collection, processing and presentation. The workshop will start with exercises of data mapping and visualization through parametric modelling tools. Surreal emerges when we represent and reproduce the SuperReal data with multi-media medium, which promotes more interactive response between clients and users. We understand the representation of SuperReal is the project itself, meaning iterative feedback from statistical database to inspirational presentations will generate the design concepts. In this workshop, we will borrow the techniques and knowledge for film, animation and game industries, to produce the super-real surreal architecture in-between the virtual and the real space. The context of our workshop will be based on the imagination of how people would use the Galaxy Soho in Beijing in 50 years from now on. As we know, the Galaxy Soho is a new icon among those most recognizable icons in the capital of China. All the icons are designed to play against the human scale as the way to respect humans. The application of the SuperReal & Surreal through multi-media tools is how to re-occupying the macro anti/pro-human iconic buildings with micro events in human scale inspired by the data mapping outputs that we produced in the early stage. Some of the most prominent features, which the participants will be exposed to during AAVS Beijinginclude: • Teaching team: AAVS Beijing tutors are selected from recent graduates / current tutors at the AA. Participants engage in an active learning environment where the large tutor to student ratio (5:1) allows for personalized tutorials and debates. • Facilities: AAVS Beijing is based on Tsinghua University, which offers laser cutting, CNC milling, and 3d printing facilities. • Computational skills: The toolset of AAVS Beijing includes the most advanced computational design tools, such as Rhinoceros, Maya, Digital Project, Processing, Arduino, and Grasshopper. According to the agenda of this year, it is also include InformationMapping and Multi-Media representation tools. • Theoretical understanding: The dissemination of fundamental design techniques and relevant critical thinking methodologies to the participants through theoretical sessions and seminars forms one of the major goals of AAVS Beijing. • Professional awareness: AAVS Beijing performs as a simulation of the professional environment due the priority given to team-based design approach. Participants ranging from 2nd year students to PhD candidates and full-time professionals experience a highly focused collaborative educational model, which promotes research-based design and making. • Fabrication: According to the specific agenda of each year, form node model to a one-to-one scale prototype could be fabricated and assembled by design teams. • Lecture series: Based on its unique location, Beijing, AAVS Beijing creates a vibrant atmosphere with its intense lecture programme conveying the diverse expertise of professionals from some of the world’s exciting practices in the areas of urbanization，regional and computational architecture design.
tritonminingco / Triton MiningTriton Mining Co. is an open-source deep-sea exploration initiative focused on AI-driven AUVs, seabed mapping, and environmental monitoring. This repo serves as the central hub, linking to specialized projects for AI navigation, robotics control, and real-time ocean data. Join us in building transparent, sustainable deep-sea technology.
hydroffice / Hyo2 OpenbstOpen BackScatter Toolchain (OpenBST)
