15,105 research outputs found
Learning Aerial Image Segmentation from Online Maps
This study deals with semantic segmentation of high-resolution (aerial)
images where a semantic class label is assigned to each pixel via supervised
classification as a basis for automatic map generation. Recently, deep
convolutional neural networks (CNNs) have shown impressive performance and have
quickly become the de-facto standard for semantic segmentation, with the added
benefit that task-specific feature design is no longer necessary. However, a
major downside of deep learning methods is that they are extremely data-hungry,
thus aggravating the perennial bottleneck of supervised classification, to
obtain enough annotated training data. On the other hand, it has been observed
that they are rather robust against noise in the training labels. This opens up
the intriguing possibility to avoid annotating huge amounts of training data,
and instead train the classifier from existing legacy data or crowd-sourced
maps which can exhibit high levels of noise. The question addressed in this
paper is: can training with large-scale, publicly available labels replace a
substantial part of the manual labeling effort and still achieve sufficient
performance? Such data will inevitably contain a significant portion of errors,
but in return virtually unlimited quantities of it are available in larger
parts of the world. We adapt a state-of-the-art CNN architecture for semantic
segmentation of buildings and roads in aerial images, and compare its
performance when using different training data sets, ranging from manually
labeled, pixel-accurate ground truth of the same city to automatic training
data derived from OpenStreetMap data from distant locations. We report our
results that indicate that satisfying performance can be obtained with
significantly less manual annotation effort, by exploiting noisy large-scale
training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models
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Towards Rapid Generation and Visualisation of Large 3D Urban Landscapes for Mobile Device Navigation
In this paper a procedural 3D modelling solution for mobile devices is presented based on scripting algorithms allowing for both the automatic and also semi-automatic creation of photorealistic quality virtual urban content. The combination of aerial images, GIS data, 2D ground maps and terrestrial photographs as input data coupled with a user-friendly customized interface permits the automatic and interactive generation of large-scale, accurate, georeferenced and fully-textured 3D virtual city content, content that can be specially optimized for use with mobile devices but also with navigational tasks in mind. Furthermore, a user-centred mobile virtual reality (VR) visualisation and interaction tool operating on PDAs (Personal Digital Assistants) for pedestrian navigation is also discussed. Via this engine, the import and display of various navigational file formats (2D and 3D) is supported, including a comprehensive front-end user-friendly graphical user interface providing immersive virtual 3D navigation
Cross-View Image Matching for Geo-localization in Urban Environments
In this paper, we address the problem of cross-view image geo-localization.
Specifically, we aim to estimate the GPS location of a query street view image
by finding the matching images in a reference database of geo-tagged bird's eye
view images, or vice versa. To this end, we present a new framework for
cross-view image geo-localization by taking advantage of the tremendous success
of deep convolutional neural networks (CNNs) in image classification and object
detection. First, we employ the Faster R-CNN to detect buildings in the query
and reference images. Next, for each building in the query image, we retrieve
the nearest neighbors from the reference buildings using a Siamese network
trained on both positive matching image pairs and negative pairs. To find the
correct NN for each query building, we develop an efficient multiple nearest
neighbors matching method based on dominant sets. We evaluate the proposed
framework on a new dataset that consists of pairs of street view and bird's eye
view images. Experimental results show that the proposed method achieves better
geo-localization accuracy than other approaches and is able to generalize to
images at unseen locations
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