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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Detecting fire occurence in Southeast Asia using satellite remote sensing and machine learning
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Make images and figures colorblind friendly by swapping their colormaps
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Live season detector using a network of cameras across North America
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Near-realtime global flood mapper using automated, localized change-detection
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Identify species of a tree using an image of its leaf. Model powered by deep learning and hosted as a chatbot.
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A simulator which allocates stormwater-associated costs based on user inputs for several different rate structures and incentives.
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A deep learning system saving you from falling into the trap of wildfire risk and social inequality
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DamageMap is a system composed of rapid buildings damage assessment and a convenient user interface for result visualization.
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AIRGAP: Assessing Inequality in Risk from Global Air Pollution tool allows for the exploration and analysis of near-real time air quality and income inequality around the world based on sattelite data.
Published in Remote Sensing of Environment, 2019
This paper is about developing a scalable plant drought stress indicator using vegetation optical depth.
Recommended citation: Rao, K., Anderegg, W.R.L., Sala, A., Martínez-Vilalta, J. & Konings, A.G. (2019). Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality. Remote Sens. Environ., 227, 125–136. https://www.sciencedirect.com/science/article/pii/S0034425719301208
Published in New Phytologist, 2019
This paper presents a short review for the use of microwave remote sensing of plant water.
Recommended citation: Konings, A.G., Rao, K. & Steele‐Dunne, S.C. (2019). Macro to Micro: Microwave Remote Sensing of Plant Water Content for Physiology and Ecology. New Phytol., nph.15808. https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.15808
Published in Remote Sensing of Environment, 2020
This paper presents a deep learning-based solution to rapidly estimate forest dryness across western USA.
Recommended citation: Rao, K., Williams, A.P., Fortin, J. & Konings, A.G. (2020). SAR-enhanced mapping of live fuel moisture content. Remote Sens. Environ., 245. https://www.sciencedirect.com/science/article/pii/S003442572030167X
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This talk presented retrofitting measures for the storwater drainage system of the IIT Madras campus. The 650-acre campus’ stormwater drainage network is more than 50 years old fails frequently during big storms. The talk focussed on presenting a 3-phase expansion plan to include-
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