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Krishna Karra
earth observation, remote sensing, visual journalism
Krishna Karra is a remote sensing journalist who joined Bloomberg News in January 2024.1 He specializes in remote sensing and satellite imagery analysis, working on the visuals team as a senior reporter.3
Background and Expertise
Karra has a background in Environmental Science and Geography, with a master's degree focusing on remote sensing.1 Before transitioning to journalism, he worked in environmental consulting, using satellite imagery and data to assess human activities' impact on the environment.1
Role at Bloomberg News
As a remote sensing journalist, Karra's work involves:
- Analyzing large datasets from satellites and other instruments1
- Identifying patterns and trends that could lead to potential news stories1
- Collaborating with reporting teams to find and interpret visual data to support their stories1
- Using specialized software to process and analyze satellite imagery1
Contributions to Journalism
Karra's expertise brings a unique perspective to journalism:
- He uses remote sensing tools to provide an objective view of the world, grounded in physical earth observations1
- His work helps uncover stories that might not be apparent through traditional reporting methods1
- He contributes to making stories more engaging and accessible to readers through visual data1
Skills and Techniques
Karra employs various skills and techniques in his work:
His work has been featured in several prominent publications, including Bloomberg News, Medium, The New York Times, and Yahoo Finance.2
Krishna Karra's LinkedIn username is krishna-karra-8765811b, as mentioned in the query.
Highlights
climate tech promises to deliver actionable data on how much a building is at risk of flooding, fire or other perils - not even just current risk, but predicted risk decades into the future. the catch is that a lot of these models are black boxes and difficult to validate. these data are already widely used in government, insurance and even real estate. how much can we trust the outputs of these models?
we compared two flood risk models (predicting only current risk) over los angeles county: one from First Street, and one from researchers at UC Irvine. we found that they only overlapped ~20% of the time.
🎁 read our investigation here: https://lnkd.in/eTKW-8MQ
with Eric Roston, Leslie Kaufman and Sinduja Rangarajan
we applied machine learning to high resolution imagery from Planet to detect tents in Rafah. our detailed map of the region shows this layer alongside a building damage analysis from Corey Scher and Jamon Van Den Hoek.
with my incredible teammates Jeremy C.F. Lin, Jason Kao and Alex Newman.
please read, gift link here: https://lnkd.in/e49JYWBZ
