Biplov Bhandari

Biplov Bhandari

Machine Learning Scientist/Engineer focused on advanced geospatial analytics, environmental hazard monitoring, and scalable ML solutions.

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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applications

Historical Flood Analysis Tool

With this tool, variable risk for floods and potentially for droughts can be found with identification of areas particularly prone to such disasters. This information can help with preparedness for prevention and response to flood disasters.

Mekong Drought and Crop Watch

The system can be used to assist local governments and the agricultural sector with seasonal drought forecasting and in implementing short and long-term mitigation measures during and in advance of droughts.

Risk Assessment Platform

The Risk Assessment Platform is an online tool that is designed to be used to perform risk assessment using the Spatial Multi-criteria Evaluation (SMCE) method. With the facilities to upload or import datasets and creating custom criteria by defining parameters and assigning weights, the tool can be utilized in different sectors. The web application uses a powerful computational resource in the background, the Google Earth Engine, to perform spatial overlay analysis using the custom criteria set by users.

Regional Land Cover & Monitoring System

This system guides users in applying peer-reviewed methods and cloud computing power to produce a wide variety of high-quality land cover information products that can be updated regularly and consistently.

Sentiel 1 Pre-processing pipeline using Sentinel Application Platform (SNAP) Graph Processing Tool (GPT) 7.0

Using Sentinel-1 SNAP GPT to download and preprocess Sentinel 1 Radar Images, and push it to the Google Cloud Bucket to import in the Google Earth Engine (GEE). The Pre-processing included applying orbit file, thermal noise removel, border noise removal, calibration, multilook correction, radiometric terrain flattening, and DEM assisted co-registration, speckle filtering, and Range-Doppler Terrain Correction. Read more here.

Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine

We investigate two methods of automatic data labeling: 1. the Joint Research Centre (JRC) surface water maps; 2. an Edge-Otsu dynamic threshold approach. We deployed a U-Net convolutional neural network to map surface water from Sentinel-1 Synthetic Aperture Radar (SAR) data and tested the model performance using different hyperparameter tuning combinations to identify the optimal learning rate and loss function. Read more here.

publications

Primitives as building blocks for constructing land cover maps.

Published in International Journal of Applied Earth Observation and Geoinformation, 2019

Recommended citation: Saah, David, Karis Tenneson, Ate Poortinga, Quyen Nguyen, Farrukh Chishtie, Khun San Aung, Kel N. Markert et al. "Primitives as building blocks for constructing land cover maps." International Journal of Applied Earth Observation and Geoinformation 85 (2020): 101979.
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Operational Flood Risk Index Mapping for Disaster Risk Reduction Using Earth Observations and Cloud Computing Technologies: A Case Study on Myanmar.

Published in Frontiers in Environmental Science, 2019

Recommended citation: Phongsapan, Kittiphong, Farrukh Chishtie, Ate Poortinga, Biplov Bhandari, Chinaporn Meechaiya, Thannarot Kunlamai, Khun San Aung et al. "Operational flood risk index mapping for disaster risk reduction using Earth Observations and cloud computing technologies: A case study on Myanmar." Frontiers in Environmental Science (2019): 191.
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Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine.

Published in ISPRS Open Journal of Photogrammetry and Remote Sensing, 2021

Recommended citation: Poortinga, Ate, Nyein Soe Thwal, Nishanta Khanal, Timothy Mayer, Biplov Bhandari, Kel Markert, Andrea P. Nicolau et al. "Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine." ISPRS Open Journal of Photogrammetry and Remote Sensing 1 (2021): 100003.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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