The NEOSSat is a dual-mission microsatellite designed to detect potentially hazardous Earth-orbit-crossing asteroids and track objects that reside in deep space

Overview

Space Apps 2019 Finalists Submission: AstroTech

What is the NEOSSat Challenge?

The NEOSSat is a dual-mission microsatellite designed to detect potentially hazardous Earth-orbit-crossing asteroids and track objects that reside in deep space. At Space Apps 2019, a competition hosted by NASA and the Canadian Space Agency (CSA) we took on the NEOSSat challenge. More info regarding the challenge can be found here: http://www.asc-csa.gc.ca/eng/events/2019/space-apps-2019.asp#challenge-3.

Who are we and what is AstroTech?

We are the finalists from Toronto of NASA and the CSA's 2019 Space Apps Challenge. Our team is composed of John Salib, Mustafa Khan, Aditya Mehrotra, Ankit Batra, Victor Qian, and Ahmed Mosehli.

Our goal was to detect and visualize asteroids moving in space using data from the NEOSSat. We call our solution: AstroTech.

AstroTech employs Edge Segmentation and False Colour Images in order to detect and effectively visualize asteroids in space.

Using Open CV's Canny Edge Detection, we made a program to detect the edges of each of the distinguishable objects in two dataset.

Additionally, NEOSSAT's raw grayscale images are used to generate a series of false colour images using the viridis colourmap (a perceptually uniform colourmap). They are then combined sequentially into a video. This allows the viewer to easily browse through the NEOSSAT dataset visually.

Where can you find our work?

The final video submission for national judging: https://www.youtube.com/watch?v=dkfggBqCmcA&feature=youtu.be.

You can visit the website for AstroTech here: https://astrotech.netlify.com.

Owner
John Salib
I am a second-year Engineering Science student at the University of Toronto, aiming to specialize in the aerospace engineering option.
John Salib
PyTorch code for the paper "FIERY: Future Instance Segmentation in Bird's-Eye view from Surround Monocular Cameras"

FIERY This is the PyTorch implementation for inference and training of the future prediction bird's-eye view network as described in: FIERY: Future In

Wayve 406 Dec 24, 2022
Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Regularizing Generative Adversarial Networks under Limited Data [Project Page][Paper] Implementation for our GAN regularization method. The proposed r

Google 148 Nov 18, 2022
PiRapGenerator - Make anyone rap the digits of pi

PiRapGenerator Make anyone rap the digits of pi (sample files are of Ted Nivison

7 Oct 02, 2022
An SE(3)-invariant autoencoder for generating the periodic structure of materials

Crystal Diffusion Variational AutoEncoder This software implementes Crystal Diffusion Variational AutoEncoder (CDVAE), which generates the periodic st

Tian Xie 94 Dec 10, 2022
This is the official implementation of TrivialAugment and a mini-library for the application of multiple image augmentation strategies including RandAugment and TrivialAugment.

Trivial Augment This is the official implementation of TrivialAugment (https://arxiv.org/abs/2103.10158), as was used for the paper. TrivialAugment is

AutoML-Freiburg-Hannover 94 Dec 30, 2022
PyTorch implementation of our paper: Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition

Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition, arxiv This is a PyTorch implementation of our paper. 1. Re

DamoCV 11 Nov 19, 2022
Self-Supervised Learning with Kernel Dependence Maximization

Self-Supervised Learning with Kernel Dependence Maximization This is the code for SSL-HSIC, a self-supervised learning loss proposed in the paper Self

DeepMind 29 Dec 29, 2022
Official PyTorch implementation of RIO

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection Figure 1: Our proposed Resampling at image-level and obect-

NVIDIA Research Projects 17 May 20, 2022
Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Paper | Blog OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene

OFA Sys 1.4k Jan 08, 2023
This is the pytorch code for the paper Curious Representation Learning for Embodied Intelligence.

Curious Representation Learning for Embodied Intelligence This is the pytorch code for the paper Curious Representation Learning for Embodied Intellig

19 Oct 19, 2022
SSD-based Object Detection in PyTorch

SSD-based Object Detection in PyTorch 서강대학교 현대모비스 SW 프로그램에서 진행한 인공지능 프로젝트입니다. Jetson nano를 이용해 pre-trained network를 fine tuning시켜 차량 및 신호등 인식을 구현하였습니다

Haneul Kim 1 Nov 16, 2021
💡 Type hints for Numpy

Type hints with dynamic checks for Numpy! (❒) Installation pip install nptyping (❒) Usage (❒) NDArray nptyping.NDArray lets you define the shape and

Ramon Hagenaars 377 Dec 28, 2022
Picasso: a methods for embedding points in 2D in a way that respects distances while fitting a user-specified shape.

Picasso Code to generate Picasso embeddings of any input matrix. Picasso maps the points of an input matrix to user-defined, n-dimensional shape coord

Pachter Lab 45 Dec 23, 2022
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding

Rot-Pro : Modeling Transitivity by Projection in Knowledge Graph Embedding This repository contains the source code for the Rot-Pro model, presented a

Tewi 9 Sep 28, 2022
ReGAN: Sequence GAN using RE[INFORCE|LAX|BAR] based PG estimators

Sequence Generation with GANs trained by Gradient Estimation Requirements: PyTorch v0.3 Python 3.6 CUDA 9.1 (For GPU) Origin The idea is from paper Se

40 Nov 03, 2022
This is the official code for the paper "Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision"

RUAS This is the official code for the paper "Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision" A prelimin

Vision & Optimization Group (VOG) 2 May 05, 2022
This project provides the proof of the uniqueness of the equilibrium and the global asymptotic stability.

Delayed-cellular-neural-network This project provides the proof of the uniqueness of the equilibrium and the global asymptotic stability. There is als

4 Apr 28, 2022
CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss This is official implement of "

程星 87 Dec 24, 2022
Python code for the paper How to scale hyperparameters for quickshift image segmentation

How to scale hyperparameters for quickshift image segmentation Python code for the paper How to scale hyperparameters for quickshift image segmentatio

0 Jan 25, 2022
Temporal Knowledge Graph Reasoning Triggered by Memories

MTDM Temporal Knowledge Graph Reasoning Triggered by Memories To alleviate the time dependence, we propose a memory-triggered decision-making (MTDM) n

4 Sep 25, 2022