Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixel-wise labelling of aerial imagery. The UNet leads to more advanced design in Aerial Image Segmentation. Future updates will gradually apply those methods to this repository.
The original sample has been preprocessed into 1000×1000 with a 1.5-meter resolution. The following image shows the models prediction on the RGB images.
Programming details are updated on Github repos.
The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixel-wise labelling of aerial imagery (link to paper). Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified colour imagery with a spatial resolution of 0.3 m Ground truth data for two semantic classes: building and not building (publicly disclosed only for the training subset) The images cover dissimilar urban settlements, ranging from densely populated areas (e.g., San Francisco’s financial district) to alpine towns (e.g,. Lienz in Austrian Tyrol).
Instead of splitting adjacent portions of the same images into the training and test subsets, different cities are included in each of the subsets. For example, images over Chicago are included in the training set (and not on the test set) and images over San Francisco are included on the test set (and not on the training set). The ultimate goal of this dataset is to assess the generalization power of the techniques: while Chicago imagery may be used for training, the system should label aerial images over other regions, with varying illumination conditions, urban landscape and time of the year.
The dataset was constructed by combining public domain imagery and public domain official building footprints.
The full data set is about 21 GB. In this repo, I select the following image as examples:
In five courses, you are going learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
In this post, I would like to share my practice with Facebook’s new Detectron2 package on macOS without GPU support for street view panoptic segmentation. If you want to create the following video by yourself, this post is all you need. This demo video clip is from my car’s dashcam footages from Preston, Melbourne. I used the PyTorch and Detectron2 to create this video with segmentation masks.
Phase 1: Problem assessment – Determine the problem’s characteristics.
What is an intelligent system?
The process of building Intelligent knowledge-based system has been called knowledge engineering since the 80s. It usually contains six phases: 1. Problem assessment; 2. Data and knowledge acquisition; 3. Development of a prototype system; 4. Development of a complete system; 5. Evaluation and revision of the system; 6. Integration and maintenance of the system .
The original program is written in Python, and uses [PyTorch], [SciPy]. A GPU is not necessary but can provide a significant speedup especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.
More details about the algorithm could be found in the following papers:
This post is talking about how to setup a basic developing environment of Google’s TensorFlow on Windows 10 and apply the awesome application called “Image style transfer”, which is using the convolutional neural networks to create artistic images based on the content image and style image provided by the users.
The early research paper is “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge on arXiv. You could also try their website application on DeepArt where there are a lot of amazing images uploaded by the people all over the world. DeepArt has apps on Google Play and App Store, but I suggest you use a much faster app Prisma, which is as awesome as DeepArt! [More to read: Why-does-the-Prisma-app-run-so-fast].
Generative models, like Generative Adversarial Networks (GAN), are a rapidly advancing area of research for computer science and machine intelligence nowadays. It’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers have achieved and been working on.
The following figures demonstrate some results of the current works ( Images from https://blog.openai.com/generative-models/).
I think it is necessary to understand the basic pros and cons of it, and it may be very helpful to your own research. I have not fully reviewed the theory and papers, but after skimmed a few papers, I got the impression that the training process of GAN models is very tricky as well as any neural networks model. Thus, there must be a huge improving space for people to make.
Thanks to the internet! There are papers and codes everywhere and nobody will be left behind in these days unless he/she wants to. So working hard and to be a better man (or women or anything good for humanity), cheers!
Here are some papers and blogs that summarized the literature very well.