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Artwork for Earthsight

Earthsight

cr458
Earth Observation
Machine Learning
Oil Palm
Earth Engine
Spatial Intelligence
Remote Sensing
Data Science
NDVI
Landsat
Sentinel-2
Change Detection
Geoai
Malaysia
Validation
Indonesia
Descartes Labs
Weather Data
Geospatial Analytics
Embedding Fields
Vegetation Mapping

Chris and Krishna share big thoughts about the geospatial industry. Chris is a data scientist currently working in weather, but has previously worked at Los Alamos National Lab, The Earth Genome, Gro Intelligence and Demeter Labs. At those places he used earth observation data to solve problems such as crop mapping, yield prediction, change detection etc... Krishna is a data journalist who uses sa... more

PublishesMonthlyEpisodes7Founded6 months ago
Number of ListenersCategory
Science

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Artwork for Earthsight

Latest Episodes

(00:00) Time series, Landtrendr, New Mexico, Cuba. Is medium/low-resolution imagery underrated?

(15:00) Bayesian Deforestation Detection, operation-oriented algorithms and platforms

(27:00) Landtrendr in the West Bank, CCDC

(35:00) Anomalous Chang... more

No pre-prepared discussion subject this time, we just winged it.

(00:00) Imagine useful geospatial models, it's easy if you try.

(08:00) Academic papers, life at the paper mill, distilled .pub for geospatial.

(24:30) Weather data vs earth observat... more

(00:00) Differences between medium-res and high-res worlds: data sizes, off-nadir imaging, co-registration issues

(28:42) High-res SAR/Umbra data detour: building damage estimation in Jamaica, matching imagery up to vector datasets

(34:33) Embeddin... more

(00:00) Experimental failures. Krishna won't let Chris forget about his failed SimCLR experiments.

(04:30) Geographic generalization discussion. Husky vs Malamute vs Wellpad.

(11:32) Chris fails to map coconut palm because it's hard. Read the paper... more

Chris and Krishna talk about validation as a practical part of developing geospatial analytics, focussing on their personal experiences mapping oil palm and land cover at scale.

A central theme in throughout this conversation is the use of auxiliar... more

In this episode Chris and Krishna talk a little about their career paths, reply to some questions from listeners about Episode 1 concerning embeddings, and profess their undying love for Google Earth Engine.

Throughout this conversation, a common th... more

Chris and Krishna discuss their experiences working with geospatial embeddings, search and remote sensing in general, and how they think through problems.

Timestamps sustainably sourced and hand crafted.

(0:00) Intro

(2:30) Background, earth obser... more

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Hosts

Chris
Co-host and data scientist specializing in weather and geospatial analysis, with previous experience at Los Alamos National Lab and other institutions focusing on earth observation data.
Krishna
Co-host, data journalist, and adjunct professor utilizing satellite imagery and geospatial data for news coverage, formerly associated with various geospatial organizations.

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Talking Points

Recent interactions between the hosts and their guests.

Krishna talks high-resolution imagery, Chris asks questions
Q: So how did you go about solving these problems?
A lot of pair-wise analysis and visual manual adjustments were used for alignment of high-resolution images.
Krishna talks high-resolution imagery, Chris asks questions
Q: What do you think of as high res?
Sub one meter is what I consider to be a useful high res.
Geospatial Platforms, Career Paths, Questions from Ep 1.
Q: What were some of the applications where concatenating before and after vectors worked well?
Concatenating before and after vectors effectively captured changes like new KFOs and mining activity in the Amazon, allowing you to identify changes in land use.
Geospatial Platforms, Career Paths, Questions from Ep 1.
Q: Can you expand more on how you generated what you call the expert embeddings one without any machine learning?
The expert embeddings are generated by picking reducers that capture relevant temporal information from a time stack of imagery, and applying similar techniques spatially for different tiles.
Geospatial Platforms, Career Paths, Questions from Ep 1.
Q: What do you think a platform should have in terms of features, and what do you think it should enable a person to do?
The platform should prioritize scale, ease of use with low cognitive burden, and the ability to handle both spatial and temporal datasets effectively.

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Frequently Asked Questions About Earthsight

What is Earthsight about and what kind of topics does it cover?

Focused on the intricacies of the geospatial industry, the conversation centers around topics such as earth observation, data analysis, and the intersection of technology and environmental issues. The hosts regularly critique the limitations of current geospatial models, while also highlighting advancements in AI tools and the application of weather data in various settings. Episodes often delve into case studies illustrating the practical applications and challenges faced in mapping and analyzing real-world phenomena like deforestation and agricultural practices. Unique insights stem from the hosts' combined experiences in data science and journalism, offering a multifaceted perspective that appeals to listeners interested in environmental... more

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1. Hard Fork

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Earthsight launched 6 months ago and published 7 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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