7,989 research outputs found
Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation
Road network extraction from satellite images is widely applicated in
intelligent traffic management and autonomous driving fields. The
high-resolution remote sensing images contain complex road areas and distracted
background, which make it a challenge for road extraction. In this study, we
present a stacked multitask network for end-to-end segmenting roads while
preserving connectivity correctness. In the network, a global-aware module is
introduced to enhance pixel-level road feature representation and eliminate
background distraction from overhead images; a road-direction-related
connectivity task is added to ensure that the network preserves the graph-level
relationships of the road segments. We also develop a stacked multihead
structure to jointly learn and effectively utilize the mutual information
between connectivity learning and segmentation learning. We evaluate the
performance of the proposed network on three public remote sensing datasets.
The experimental results demonstrate that the network outperforms the
state-of-the-art methods in terms of road segmentation accuracy and
connectivity maintenance
Promoting Connectivity of Network-Like Structures by Enforcing Region Separation
We propose a novel, connectivity-oriented loss function for training deep
convolutional networks to reconstruct network-like structures, like roads and
irrigation canals, from aerial images. The main idea behind our loss is to
express the connectivity of roads, or canals, in terms of disconnections that
they create between background regions of the image. In simple terms, a gap in
the predicted road causes two background regions, that lie on the opposite
sides of a ground truth road, to touch in prediction. Our loss function is
designed to prevent such unwanted connections between background regions, and
therefore close the gaps in predicted roads. It also prevents predicting false
positive roads and canals by penalizing unwarranted disconnections of
background regions. In order to capture even short, dead-ending road segments,
we evaluate the loss in small image crops. We show, in experiments on two
standard road benchmarks and a new data set of irrigation canals, that convnets
trained with our loss function recover road connectivity so well, that it
suffices to skeletonize their output to produce state of the art maps. A
distinct advantage of our approach is that the loss can be plugged in to any
existing training setup without further modifications
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
We address the problem of semantic nighttime image segmentation and improve
the state-of-the-art, by adapting daytime models to nighttime without using
nighttime annotations. Moreover, we design a new evaluation framework to
address the substantial uncertainty of semantics in nighttime images. Our
central contributions are: 1) a curriculum framework to gradually adapt
semantic segmentation models from day to night through progressively darker
times of day, exploiting cross-time-of-day correspondences between daytime
images from a reference map and dark images to guide the label inference in the
dark domains; 2) a novel uncertainty-aware annotation and evaluation framework
and metric for semantic segmentation, including image regions beyond human
recognition capability in the evaluation in a principled fashion; 3) the Dark
Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight
images with correspondences to their daytime counterparts plus a set of 201
nighttime images with fine pixel-level annotations created with our protocol,
which serves as a first benchmark for our novel evaluation. Experiments show
that our map-guided curriculum adaptation significantly outperforms
state-of-the-art methods on nighttime sets both for standard metrics and our
uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals
that selective invalidation of predictions can improve results on data with
ambiguous content such as our benchmark and profit safety-oriented applications
involving invalid inputs.Comment: IEEE T-PAMI 202
Persistent Homology with Improved Locality Information for more Effective Delineation
We present a new, more effective way to use Persistent Homology (PH), a
method to compare the topology of two data sets, for training deep networks to
delineate road networks in aerial images and neuronal processes in microscopy
scans. Its essence is in a novel filtration function, derived from a fusion of
two existing techniques: thresholding-based filtration, previously used to
train deep networks to segment medical images, and filtration with height
functions, used before for comparison of 2D and 3D shapes. We experimentally
demonstrate that deep networks trained with our Persistent-Homology-based loss
yield reconstructions of road networks and neuronal processes that preserve the
connectivity of the originals better than existing topological and
non-topological loss functions
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