Artwork

Contenuto fornito da MapScaping. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da MapScaping o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
Player FM - App Podcast
Vai offline con l'app Player FM !

Computer Vision and GeoAI

37:58
 
Condividi
 

Manage episode 361736945 series 2502116
Contenuto fornito da MapScaping. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da MapScaping o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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 episodi

Artwork
iconCondividi
 
Manage episode 361736945 series 2502116
Contenuto fornito da MapScaping. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da MapScaping o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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 episodi

모든 에피소드

×
 
Loading …

Benvenuto su Player FM!

Player FM ricerca sul web podcast di alta qualità che tu possa goderti adesso. È la migliore app di podcast e funziona su Android, iPhone e web. Registrati per sincronizzare le iscrizioni su tutti i tuoi dispositivi.

 

Guida rapida