mprakhar Goto Github PK

aakash

“An Interdisciplinary Study toward Clean Air, Public Health and Sustainable Agriculture: The Case of Crop Residue Burning in North India”

airrgb

aq

awesome-deep-learning-papers

The most cited deep learning papers

bk_atr

brick_kiln_detection

dataassimilation

dsm2dtm

Code for the paper - Comparison of Digital Building Height Models Extracted from AW3D, TanDEM-X, ASTER, and SRTM Digital Surface Models over Yangon City

firecam

FIRECAM: Fire Inventories - Regional Evaluation, Comparison, and Metrics

global-river-ice-dataset-from-landsat

Python (Google Earth Engine), JavaScript (Google Earth Engine) and R code to extract river ice condition from Landsat satellites, to develop empirical model, and to predict future changes in river ice

hackathon_datasources

Geospatial Data Sources of Interest for Artathon 2017 event

ipython-notebooks

A collection of IPython notebooks covering various topics.

jupyter

Jupyter metapackage for installation, docs and chat

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

modl2t-burned-area

ModL2T: hybrid MODIS and Landsat algorithm in Google Earth Engine for estimating post-monsoon burned area from agricultural fires in northwestern India

mprakhar.github.io

page for academic updates https://mprakhar.github.io/

pdf2csv

Code to convert power consumption report from the POSOCO generated in .pdf to .csv for analysis.

policyspace

Agent-based model

productivity

raster_utils

Collection of codes to manipulate georasters

s1_ard

repository for testing analysis-readiness of Sentinel-1 RTC backscatter

skynet-train

Training and test the SegNet neural network on satellite imagery

smoke-policy-tool

SMOKE Policy Tool: Model and project the impact of Indonesian fires on public health in Equatorial Asia for 2005-2029 based on land use/ land cover classification, GFEDv4s fire emissions, and meteorology in Google Earth Engine

urban-environments

Code for constructing the urban environments dataset and for land use classification via convolutional neural networks

urbanmorphology

wtlab_rs