Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark

Abstract

Over the recent years, there has been an increasing interest in large-scale classification of remote sensing images. In this context, the Inria Aerial Image Labeling Benchmark has been released online in December 2016. In this paper, we discuss the outcomes of the first year of the benchmark contest, which consisted in dense labeling of aerial images into building / not building classes, covering areas of five cities not present in the training set. We present four methods with the highest numerical accuracies, all four being convolutional neural network approaches. It is remarkable that three of these methods use the U-net architecture, which has thus proven to become a new standard in image dense labeling.

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

“…First, because U‐net has a demonstrated high performance for very high resolution image segmentation [this study and Huang et al. ()]; second, because it enables production of a vegetation mask at very high resolution; and finally, because U‐net is relatively easy and convenient to use.…”

“…This U‐net model has recently proven to become a new standard in image dense labeling (Huang et al. ). We adapted the U‐net architecture from Ronneberger et al.…”

“…This network performs a per-pixel classification, predicting the probability of each pixel to belong to a particular class. This U-net model has recently proven to become a new standard in image dense labeling (Huang et al 2018). We adapted the U-net architecture from Ronneberger et al (2015) with twice less filters, since our training set is limited and a smaller number of filters helps in preventing overfitting.…”

“…; Huang et al. ). The architecture of this network consists of a contracting path to capture context and a symmetric expanding path that enables precise localization (Ronneberger et al.…”

“…These deep learning algorithms are used for land cover classification (Arief et al 2018;Sun et al 2018), scene classification (Maggiori et al 2017;Wang et al 2017;Liu et al 2018) and object extraction (Xu et al 2018). One particular type of network used for object extraction, the Unet network, is highly promising as it has been shown to outperform all traditional classification methods (Ronneberger et al 2015;Huang et al 2018). The architecture of this network consists of a contracting path to capture context and a symmetric expanding path that enables precise localization (Ronneberger et al 2015).…”