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Reading List

Contents

1. Atmosphric Sciences

2. Hydrology

3. Remote Sensing

4. Computer Vision

4.1 Machine Learning Basics
4.2 Object Detection
4.3 ML/DL for Weather Prediction
4.4 ML/DL for Rainfall Estimation
4.5 Generative Adversaril Network

5. Numerical Models

5.1 Optical Flow
5.2 Semi-Lagrangian Scheme

6. Statistics

7. Updates

Atmospheric Sciences

  1. Atmospheric Science by JONH M. WALLACE and PETER V. HOBBS
  2. Python Gallery for meterology
  3. A Review of Global Precipitation Datasets: Data Sources, Estimation, and Intercomparisons

Remote Sensing

  1. Hydrologic Remote Sensing: Capacity Building for Sustainability and Resilience

Hydrology (notes)

  1. 23 Unsolved problems in hydrology UPH a community perspective

Computer Vision

Machine Learning Basics

  1. 神经网络与深度学习
  2. Standford CS231: Convolutional Neural Networks for Visual Recognition
  3. Learning kernels for CV 3.1 Orientation Filter: Gabor Filter Interesting intro; opencv
  4. Collection of Pytorch Lists

Object Detection/Tracking Generalized

  1. Gaussian Mixture Models (GMM)

ML/DL for weather prediction (notes)

  1. Generating Videos with Scene Dynamics/GAN
  2. A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
  3. Deep Learning for Precipitation Nowcasting: A benchmark and A new model
  4. Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

ML/DL for rainfall estimation (notes)

  1. Video-based rainfall removal

    1.1 Video Rain Streak Removal By Multiscale ConvolutionalSparse Coding

    1.2 Is it Raining Outside?Detection of Rainfall using General-Purpose Surveillance Cameras | code

  2. Image-based rainfall removal

    2.1 JORDEN: Deep Joint Rain Detection from a Single Image

    2.2 Rain Streaks Detection and Removal in Image based on Entropy Maximization and Background Estimation

    2.3 Spatial Attentive Single-Image Deraining with a High Quality Real Rain dataset

    2.4 Dynamic Routing Residue Recurrent Network for Video Removal

    2.5 Progressive Image Deraining Networks: A Better and Simpler Baseline

Generative Adversaril Network(GAN) (notes)

  1. Pytorch-GAN-zoo
  2. InfoGAN
  3. tempoGAN
  4. WGAN
  5. WGAN-improved | github
  6. DehazeGAN | code
  7. Glow | Blog | github

Numerical Methods

Optical Flow

Methods:

Lucas-Kanade method: it tracks the corner with Shi-Tomasi algorithm and calculate (u,v) by solving 9 equations and then estimate the 3x3 patch movement;

cv2.calcOpticalFlowPyrLK

Gunner Farneback's algorithm (Dense): It computes the optical flow for all the points in the frame;

cv2.calcOpticalFlowFarneback
  1. Optical flow models as an open benchmark for radar-based precipitation nowcasting

    Codes available on github: optical flow

  2. Novel Video Prediction for Large-scale Scene using Optical Flow
  3. Two Frame Motion Estimation Based on Polynomial Expansion
  4. my radar project demo
  5. FlowNet: Learning Optical Flow with Convolutional Networks

Semi-Lagrangian Scheme

  1. My radar project demo

Statistics

  1. Probabilistic Programming and Bayesian Methods for Hackers

Updates

  • Migrate to library
  • Update hydrology (2019.6.16)
  • Update GAN collections and rainfall removal category(2019.6.2)
  • FlowNet: Learning Optical Flow with Convolutional Networks(2019.5.23)
  • 神经网络与深度学习 (2019.5.22)
  • optical flow models as an open benchmark for radar-based precipitation nowcasting (2019.5.16)
  • Novel Video Prediction for Large-scale Scene using Optical Flow (2019.5.16)
  • Generating Videos with Scene Dynamics/GAN (2019.5.16)
  • Two Frame Motion Estimation Based on Polynomial Expansion