When deep learning meets satellite imagery 

This reminds me of the way Johny Srouji presents a new Apple Silicon architecture in the Apple keynotes. Very precise, focused and polished.

This is the best video on the subject. Please set up a Udemy course on deep learning and GIS satellite imagery.

Thanks Julie and Earthcube Team!

Good ideas in the presentation. For handling the contrast of the colour, you could convert all photos to black and white for training and testing. I never considered cutting images into smaller sizes and processing them as multiple tests of the same image, I had only considered cropping images to where the objects were most likely to be. Thanks for sharing.

Awesome explanation! 😊 Looking forward to learning more

Great Channel. Great Idea. Her accent sounds great for American Ears. Very attractive rhythm to listen too.

Deep learning seems like a perfect match for hyperspectral imaging.

Next generation frenglish + 99% of the energy spent in this video got consumed by the very expressive random hand motion :D Thanks for the content.

So concise and clear.

Awesome content. Thanks!

Precise explanation. Thank you very much

Love this video!

Another very well designed and explained video, great job!

Nice Explanation, worth watching, very informative

So many relevant information in this video. Thank you so much

Loved this

Where can I learn more about these topics ? Im a fourth year astronomy student and find this super interesting

Thank you for the video. Somehow I’m a bit disappointed, but it’s my fault. From the title, I expected to hear how deep learning is complementing convolutional networks, but deep learning is mentioned just superficially. Maybe a next video?

Great Explanation, Very simply explained Thanks a lot!

Great!

Le vocal est très bien réussi ainsi que la présentation. J'ai beaucoup aimé

Hi Julie, I listened to your RU-vid video with great interest and found it extremely professional and informative...really outstanding job! I was hoping you may be able to answer a couple of questions I have that are inspired by your presentation: Given: - NVIDIA's plans to build its "Eos" supercomputer, a machine expected to be the world’s fastest AI system after it begins operations later this year, and its advertised specifications: -- 18.4 exaflops of AI computing performance -- 275 petaflops of traditional scientific computing performance - the current state of the art deep learning algorithms as they apply to satellite images; be they Earthcube's, Preligens' or a widely accepted performance standard - the following Worldview-3's Specs: -- 0.31m Pan Sensor resolution (aka GSD, Ground Sample Distance; off-nadir is geometric mean) -- 680,000 sq km capacity per day 1. How many small tiles do you estimate would have to be created in order to be processed by the current neural networks? 2. How long do you estimate it take NVIDIA's Eos to process the number of tiles estimated in question one? 3. On average, how many distinct objects are classified by the current neural networks used in commercial or laboratory settings? 4. Looking ten years out, how many distinct objects do you think neural networks, coupled with forecasted improvements in conventional (non-quantum) supercomputering, will be able to simultaneously classify? I sincerely appreciate your time and please don't spend too much of it figuring out highly informed answers. I'm just looking for "ballpark" figures from a recognized expert in the field. If you have a Patreon or a PayPal account, I am more than willing to entertain compensation for the time you spend answering my questions. I wish you and yours the best, and I hope all are safe, happy, and healthy. :-) Thank you and kind regards, Rob Reference: nvidianews.nvidia.com/news/nvidia-announces-dgx-h100-systems-worlds-most-advanced-enterprise-ai-infrastructure

Thanks for sharing this wonderful video

very nice video and the articulation is very good.

Very useful video & it seems nice company

Good work!! thank you

Wonderful video

This was a great watch! Kudos to the whole team!

Great explanation and information. Thank you

Awesome explanation.Thank you for sharing

This is a great video. Thanks!

great presentation.. i was looking this

Which software can provide satelite digitization with AI?

Thanks for the video!

Wonderful 👏

This was fascinating

Very good presentation

The source from witch to attract satellite imagery is limited or limited to just, say one satellite? To have a off-Nadir is sometimes better than Nadir, for instance knowing height on a building. Also is there any consideration of privacy...

Great lectures

Multiple images for same trajectory to superimpose will give accuracy

magnifique vidéo

Does this have to do anything with geomatics?

Holy mf raw audio

i speak fluent English, but i bet it doesn't sound this smooth and beautiful!!!

what are the other channels present in the satellite images?

That was very interesting.

thanks

How she says photographee is interesting. I sometimes wonder how the hell people learn some of the random ass rules in English

From baking cupcakes, to this.... hmm.

I keep looking at the hand movement and forgot to listen. It is weird.

i came here after watching add and now i feel awsome

هل يمكن الكشف عن الذهب عن طريق الأقمار الصناعية

i like your accent

Je comprends l'intérêt de l'anglais pour partager le savoir, mais regarder une vidéo en anglais avec accent français, c'est un peu frustrant quand on comprend mieux le français que l'anglais :(

I need a project for this please help

النَّظِيرُ فِي عِلْمِ الْفَلَكِ : نُقْطَةٌ فِي السَّمَاءِ عَلَى خَطٍّ عَمُودِيٍّ مِنَ الْمُرَاقِبِ تَحْتَ قَدَمَيْهِ مُبَاشَرَةً

Are u French?

Tone is Russian language in British

Where can we download the dataset of satellite image ?

Nice accent

Really good content, but please change the host/speaker

No clue what she said.

Fun Fact: She's AI

😂

Can you see me?

произношение немного режет уши :) а так, интересный материал

The presenter is fake. She is a robot because no one with human feelings and a soul can move their arms and hands like that. Someone who controls the robot thought it was a good idea to give the AI ​​10,000 hours of a conductor's time, so that it could learn to move its arms. Unreal. It's like watching a 90s robot dance, perfect movements, zero soul. She is so histrionic that it does not help in the explanation, it simply, ..., distracts. It's like a child's performance at school.

When deep learning, meets satellite imagery, what do you get ? Fires like Hawaii that can burn steel and take 90 degree corners like a street racer. Thank you I needed to see that to truly have the pieces snap into focus, no stupid puns or jokes at all. Serious as those fires.