I’ve been working on a similar problem, and a cool approach is outlined in this paper “Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping”: https://arxiv.org/pdf/1510.00098.pdf
tldr: researches trained vgg 8 on ImageNet; then finetuned the model to predict nighttime light intensity from daytime images, which has the correlated result of optimizing for filters that pick out roads, urban areas, farms, etc; then this is used to predict poverty levels with ~70% accuracy compared to ground-level surveys
One interesting issue that didn’t come up in part 1 of the class but is significant for this challenge is reweighting the loss function. In this competition is that the model gets the most “reward” for classifying large areas like crops and roads, and then the model has little incentive to identify smaller objects that don’t occupy a large area like trucks and walls. A strategy that some competitors are trying is training different models on different classes separately.
[this post has been edited from an earlier post that had outlined a problem that ended up as a data processing error, and not a model error. However, I thought remaining part about imbalanced datasets and reweighting might still be useful to the curious. ]
Hi Everyone,
I am quite fascinate by this idea of detecting object through satellite images. I am new to image processing and deep learning, and I dont have much idea about how to deal with satellite images and how to use deep learning to identify features.
There are lot of terms in the competition that I dont understand like 16 band images, polygons etc.
Can someone point me to some resource about satellite imagery and how to process them, just like a lesson 0 (starting point).
The DSTL Kaggle competition just closed. And our 2-person team placed 22nd! The top 6% of the current competitors.
I’m hoping to take the notes I posted here eventually and our data pipeline code and bundle them together for some kind of startup guide for beginners to start playing with satellite data.
Neither of us knew how to deal with satellite data before the competition, entered a Kaggle competition, or wrote a model that does image segmentation/ semantic segmentation/ image segmentation + classification (I don’t know the right buzzwords!). But because of this class and the Kaggle’s public community, we (quickly!) learned enough to be competitive! Really big thanks @jeremy and @rachel for making democratizing the tech and nurturing this community !!!
It was very much a surprise b/c we were 123rd on the public leaderboard when the competition closed on the public dataset (19% of the total data), and then found out after the rest of the 81% of the data was calculated our prediction accuracy on the different classes of objects went up, while the leaders’ accuracy went down. We’re still trying to figure why/how that happened or if that is normal for Kaggle competitions.
I can’t wait to hear about your experiences. There’s very little material out there to help people do well in Kaggle competitions with very little previous ML background, so I think your story and tips will be helpful for many people!
Wow, big congrats, this is awesome!!
I would love to learn more what you guys have done, 22nd is pretty impressive especially this is your first time to handle satellite images.
I know I’m delayed, but congratulations! I’m definitely interested in hearing how you got there, since I’m also relatively green when it comes to kaggle competitions, and image recognition projects. Cheers.
