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

SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth Observation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4-5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks. All datasets and model weights are available via the TorchGeo (https://github.com/microsoft/torchgeo) library, making reproducibility and experimentation easy, and enabling scientific advancements in the burgeoning field of remote sensing for a multitude of downstream applications

SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth Observation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4–5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks. All datasets and model weights are available via the TorchGeo library, making reproducibility and experimentation easy, and enabling scientific advancements in the burgeoning field of remote sensing for a multitude of downstream applications

A preliminary analysis of interleukin-1 ligands as potential predictive biomarkers of response to cetuximab

The epidermal growth factor receptor (EGFR) monoclonal IgG1 antibody cetuximab is approved for first-line treatment of recurrent and metastatic (R/M) HNSCC as a part of the standard of care EXTREME regimen (platinum/5-fluorouracil/cetuximab). This regimen has relatively high response and disease control rates but is generally not curative and many patients will experience recurrent disease and/or metastasis. Therefore, there is a great need to identify predictive biomarkers for recurrence and disease progression in cetuximab-treated HNSCC patients to facilitate patient management and allow for treatment modification. The goal of this work is to assess the potential of activating interleukin-1 (IL-1) ligands (IL-1 alpha [IL-1α], IL-1 beta [IL-1β]) as predictive biomarkers of survival outcomes in HNSCC patients treated with cetuximab-based chemotherapy.2016 AACR-Bayer Innovation and Discovery Grant [16-80-44-SIMO]; National Institutes of Health (NIH) [R01DE024550, F99CA223062, R01 CA177669, P30 CA006973, P50 DE019032, T32 AI007511]; University of Iowa Department of Pathology Research Grant; University of Iowa Head and Neck Cancer Symposium Seed Grant; Johns Hopkins University Catalyst AwardOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Inhibition of Protein-protein Interactions in Mycobacterium tuberculosis

Tuberculosis is a highly contagious, infectious disease that kills about 1.8 million people annually. Current chemotherapeutic regimens are both inefficient and taxing to the patient. In addition, the disease has suboptimal treatment due to the rise of multidrug resistant strains of Mycobacterium tuberculosis (Mtb), the causative bacterial agent of tuberculosis. Therefore, we established a critical assay to identify novel drugs that interfere with specific Mtb virulence mechanisms. The mycobacterial protein fragment complementation (M-PFC) assay was developed to screen 725 compound drug panel to find candidate drugs that interfered with important virulence-causing protein interactions of Mtb. We targeted the EsxA EsxB and EsxMEsxN interactions of the type VII secretion systems of Mtb. Our screen identified 46 small molecules that inhibited both virulence interactions, exhibiting nonspecific activity against a model cell line in vitro as well as seven hits specific to one of the two cell lines. In the future, we hope to retest the seven unique positive hits to confirm their ability to inhibit specific proteinprotein interactions of Mtb