15 research outputs found
Single-View Height Estimation with Conditional Diffusion Probabilistic Models
Digital Surface Models (DSM) offer a wealth of height information for
understanding the Earth's surface as well as monitoring the existence or change
in natural and man-made structures. Classical height estimation requires
multi-view geospatial imagery or LiDAR point clouds which can be expensive to
acquire. Single-view height estimation using neural network based models shows
promise however it can struggle with reconstructing high resolution features.
The latest advancements in diffusion models for high resolution image synthesis
and editing have yet to be utilized for remote sensing imagery, particularly
height estimation. Our approach involves training a generative diffusion model
to learn the joint distribution of optical and DSM images across both domains
as a Markov chain. This is accomplished by minimizing a denoising score
matching objective while being conditioned on the source image to generate
realistic high resolution 3D surfaces. In this paper we experiment with
conditional denoising diffusion probabilistic models (DDPM) for height
estimation from a single remotely sensed image and show promising results on
the Vaihingen benchmark dataset
ZRG: A High Resolution 3D Residential Rooftop Geometry Dataset for Machine Learning
In this paper we present the Zeitview Rooftop Geometry (ZRG) dataset. ZRG
contains thousands of samples of high resolution orthomosaics of aerial imagery
of residential rooftops with corresponding digital surface models (DSM), 3D
rooftop wireframes, and multiview imagery generated point clouds for the
purpose of residential rooftop geometry and scene understanding. We perform
thorough benchmarks to illustrate the numerous applications unlocked by this
dataset and provide baselines for the tasks of roof outline extraction,
monocular height estimation, and planar roof structure extraction
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
Research in self-supervised learning (SSL) with natural images has progressed
rapidly in recent years and is now increasingly being applied to and
benchmarked with datasets containing remotely sensed imagery. A common
benchmark case is to evaluate SSL pre-trained model embeddings on datasets of
remotely sensed imagery with small patch sizes, e.g., 32x32 pixels, whereas
standard SSL pre-training takes place with larger patch sizes, e.g., 224x224.
Furthermore, pre-training methods tend to use different image normalization
preprocessing steps depending on the dataset. In this paper, we show, across
seven satellite and aerial imagery datasets of varying resolution, that by
simply following the preprocessing steps used in pre-training (precisely, image
sizing and normalization methods), one can achieve significant performance
improvements when evaluating the extracted features on downstream tasks -- an
important detail overlooked in previous work in this space. We show that by
following these steps, ImageNet pre-training remains a competitive baseline for
satellite imagery based transfer learning tasks -- for example we find that
these steps give +32.28 to overall accuracy on the So2Sat random split dataset
and +11.16 on the EuroSAT dataset. Finally, we report comprehensive benchmark
results with a variety of simple baseline methods for each of the seven
datasets, forming an initial benchmark suite for remote sensing imagery
An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph
Over the years, open-source software systems have become prey to threat
actors. Even as open-source communities act quickly to patch the breach, code
vulnerability screening should be an integral part of agile software
development from the beginning. Unfortunately, current vulnerability screening
techniques are ineffective at identifying novel vulnerabilities or providing
developers with code vulnerability and classification. Furthermore, the
datasets used for vulnerability learning often exhibit distribution shifts from
the real-world testing distribution due to novel attack strategies deployed by
adversaries and as a result, the machine learning model's performance may be
hindered or biased. To address these issues, we propose a joint interpolated
multitasked unbiased vulnerability classifier comprising a transformer
"RoBERTa" and graph convolution neural network (GCN). We present a training
process utilizing a semantic vulnerability graph (SVG) representation from
source code, created by integrating edges from a sequential flow, control flow,
and data flow, as well as a novel flow dubbed Poacher Flow (PF). Poacher flow
edges reduce the gap between dynamic and static program analysis and handle
complex long-range dependencies. Moreover, our approach reduces biases of
classifiers regarding unbalanced datasets by integrating Focal Loss objective
function along with SVG. Remarkably, experimental results show that our
classifier outperforms state-of-the-art results on vulnerability detection with
fewer false negatives and false positives. After testing our model across
multiple datasets, it shows an improvement of at least 2.41% and 18.75% in the
best-case scenario. Evaluations using N-day program samples demonstrate that
our proposed approach achieves a 93% accuracy and was able to detect 4,
zero-day vulnerabilities from popular GitHub repositories