Prakhar Misra (mprakhar)

mprakhar

Geek Repo

Location:India, Japan

Home Page:https://mprakhar.github.io/

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Prakhar Misra's repositories

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

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Aakash

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

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awesome-deep-learning-papers

The most cited deep learning papers

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FIRECAM

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

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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

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Hackathon_DataSources

Geospatial Data Sources of Interest for Artathon 2017 event

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ipython-notebooks

A collection of IPython notebooks covering various topics.

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jupyter

Jupyter metapackage for installation, docs and chat

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Mask_RCNN

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

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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

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mprakhar.github.io

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

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pdf2csv

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

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PolicySpace

Agent-based model

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raster_utils

Collection of codes to manipulate georasters

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S1_ARD

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

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skynet-train

Training and test the SegNet neural network on satellite imagery

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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

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urban-environments

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

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