Search Results for author:
Found 20 papers, 4 papers with code
Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes.
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
Various instances of the network were trained on 2D axial datasets created in different ways: (i) slice-level split and (ii) patient-level split; inputs of different types were used: (i) only PET slices and (ii) concatenated PET and CT slices; and different training strategies were employed: (i) center-aware (CAW) and (ii) center-agnostic (CAG).
Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery and few or no labeled positive samples to start with.
Weak Labeling for Cropland Mapping in Africa
We conduct experiments to demonstrate the benefits of the improved weak labels generated by our method.
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery
In this work we propose a road segmentation benchmark dataset, Chesapeake Roads Spatial Context (RSC), for evaluating the spatial long-range context understanding of geospatial machine learning models and show how commonly used semantic segmentation models can fail at this task.
Ranked #1 on
Road Segmentation
on ChesapeakeRSC
Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images
This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images.
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
In particular, we instruction tune vision-language models to generate detailed visual descriptions of camera trap images using similar terminology to experts.
Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data
To the best of our knowledge, our work is the first that: (i) proposes a training and evaluation framework that does not assume that real data is available for testing the utility and fairness of machine learning models trained on synthetic data; (ii) presents the most extensive analysis of synthetic data set generation algorithms in terms of utility and fairness when used for training machine learning models; and (iii) encompasses several different definitions of fairness.
Poverty rate prediction using multi-modal survey and earth observation data
This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region.
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts.
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.
Dwelling Type Classification for Disaster Risk Assessment Using Satellite Imagery
Then a risk score assessment model was employed, using the determined dwelling type along with an inundation model of the regions.
Fast building segmentation from satellite imagery and few local labels
Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level.
Retinal Microvasculature as Biomarker for Diabetes and Cardiovascular Diseases
Conclusion: In the case of DR, most of the disease biomarkers are related topologically to the vasculature.
An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises
Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers.
Defending Democracy: Using Deep Learning to Identify and Prevent Misinformation
The rise in online misinformation in recent years threatens democracies by distorting authentic public discourse and causing confusion, fear, and even, in extreme cases, violence.
Height Estimation of Children under Five Years using Depth Images
Malnutrition is a global health crisis and is the leading cause of death among children under five.
Metadata-Based Detection of Child Sexual Abuse Material
With such restrictions in place, the development of CSAM machine learning detection systems based on file metadata uncovers several opportunities.
Improving Lesion Detection by exploring bias on Skin Lesion dataset
Bissoto et al. experimented with different bounding-box based masks and showed that deep learning models could classify skin lesion images without clinically meaningful information in the input data.
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
In this work, we propose the first formal framework for membership privacy estimation in generative models.
