Search Results for author:
Found 13 papers, 8 papers with code
Imbalance-aware Presence-only Loss Function for Species Distribution Modeling
In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences.
Data-Centric Machine Learning for Geospatial Remote Sensing Data
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
The resulting SatCLIP location encoder efficiently summarizes the characteristics of any given location for convenient use in downstream tasks.
Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks
At the same time, little attention has been paid to the exact design of the neural network architectures with which these functional embeddings are combined.
Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2
Here, remote sensing can provide reliable estimates of plastic pollution by regularly monitoring and detecting marine debris in coastal areas.
Meta-Learning for Few-Shot Land Cover Classification
This indicates that model optimization with meta-learning may benefit tasks in the Earth sciences whose data show a high degree of diversity from region to region, while traditional gradient-based supervised learning remains suitable in the absence of a feature or label shift.
Self-attention for raw optical Satellite Time Series Classification
The amount of available Earth observation data has increased dramatically in the recent years.
Early Classification for Agricultural Monitoring from Satellite Time Series
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring.
BreizhCrops: A Time Series Dataset for Crop Type Mapping
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series.
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
Multi$^{\mathbf{3}}$Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.
Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery
Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods.
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images.
