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MapScaping द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री MapScaping या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal
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Computer Vision and GeoAI

37:58
 
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Manage episode 361736945 series 2502116
MapScaping द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री MapScaping या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images.

You might think that this is exactly what we are doing in earth observation but there are a few important differences between computer vision and what some people refer to as GeoAI.

This week Jordi inglada is going to help you understand what those differences are and why it's not always possible to use Computer vision techniques in the field of Remote Sensing.

Listen out for these key points during the conversation!

  • Why plausible or realistic data is not always a substitute for actual measurements, except when it is ;)
  • In computer vision we can learn from the data, in earth observation we know the physics
  • To do interesting work in data science you need to - Computer science, applied math, and domain expertise. You don’t need to be an expert in all three but you need to be interested in all three
  • Vectors in the machine learning world don’t necessarily have anything to do with points lines and polygons ;)

Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing. dataspace.copernicus.eu/ http://dataspace.copernicus.eu/

Related Podcast Episodes

Super Resolution

https://mapscaping.com/podcast/super-resolution-smarter-upsampling/

Fake Satellite Imagery

https://mapscaping.com/podcast/fake-satellite-imagery/

Sentinal Hub

https://mapscaping.com/podcast/sentinel-hub/

Google Earth Engine

https://mapscaping.com/podcast/introducing-google-earth-engine/

Microsofts Planetary Computer

https://mapscaping.com/podcast/the-planetary-computer/

BTW MapScaping has started a Job Board!

it's in the early stages but it's live

Jobs - Mapscaping.com

Some more episodes you might enjoy

ESRI, GIS careers, Geospatial Data Science

QGIS, Geospatial Python, ArcGIS Pro

Google Maps, Geomatics, Cartography

Location Intelligence, Mapping

  continue reading

238 एपिसोडस

Artwork
iconसाझा करें
 
Manage episode 361736945 series 2502116
MapScaping द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री MapScaping या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images.

You might think that this is exactly what we are doing in earth observation but there are a few important differences between computer vision and what some people refer to as GeoAI.

This week Jordi inglada is going to help you understand what those differences are and why it's not always possible to use Computer vision techniques in the field of Remote Sensing.

Listen out for these key points during the conversation!

  • Why plausible or realistic data is not always a substitute for actual measurements, except when it is ;)
  • In computer vision we can learn from the data, in earth observation we know the physics
  • To do interesting work in data science you need to - Computer science, applied math, and domain expertise. You don’t need to be an expert in all three but you need to be interested in all three
  • Vectors in the machine learning world don’t necessarily have anything to do with points lines and polygons ;)

Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing. dataspace.copernicus.eu/ http://dataspace.copernicus.eu/

Related Podcast Episodes

Super Resolution

https://mapscaping.com/podcast/super-resolution-smarter-upsampling/

Fake Satellite Imagery

https://mapscaping.com/podcast/fake-satellite-imagery/

Sentinal Hub

https://mapscaping.com/podcast/sentinel-hub/

Google Earth Engine

https://mapscaping.com/podcast/introducing-google-earth-engine/

Microsofts Planetary Computer

https://mapscaping.com/podcast/the-planetary-computer/

BTW MapScaping has started a Job Board!

it's in the early stages but it's live

Jobs - Mapscaping.com

Some more episodes you might enjoy

ESRI, GIS careers, Geospatial Data Science

QGIS, Geospatial Python, ArcGIS Pro

Google Maps, Geomatics, Cartography

Location Intelligence, Mapping

  continue reading

238 एपिसोडस

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