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Found 22 papers, 9 papers with code
XAI for Early Crop Classification
We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods.
Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring
With leaps in machine learning techniques and their applicationon Earth observation challenges has unlocked unprecedented performance across the domain.
EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of acquisition and harmonisation.
[Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
Additionally, we also compiled an alternative dataset similar to the one presented in the paper and evaluated the methodology on it.
Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification
Human Settlement Extent (HSE) and Local Climate Zone (LCZ) maps are both essential sources, e. g., for sustainable urban development and Urban Heat Island (UHI) studies.
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.
A Generalized Multi-Task Learning Approach to Stereo DSM Filtering in Urban Areas
Such basic models can be filtered by convolutional neural networks (CNNs), trained on labels derived from digital elevation models (DEMs) and 3D city models, in order to obtain a refined DSM.
Self-attention for raw optical Satellite Time Series Classification
The amount of available Earth observation data has increased dramatically in the recent years.
Enhancing Traffic Scene Predictions with Generative Adversarial Networks
This ensures the quality of the predicted frames to be sufficient to enable accurate detection of objects, which is especially important for autonomously driving cars.
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.
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task.
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.
Late or Earlier Information Fusion from Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-cGAN
We present the workflow of a DSM refinement methodology using a Hybrid-cGAN where the generative part consists of two encoders and a common decoder which blends the spectral and height information within one network.
DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
We describe the workflow of a digital surface models (DSMs) refinement algorithm using a hybrid conditional generative adversarial network (cGAN) where the generative part consists of two parallel networks merged at the last stage forming a WNet architecture.
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.
FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANs
The main advantage of the FutureGAN framework is that it is applicable to various different datasets without additional changes, whilst achieving stable results that are competitive to the state-of-the-art in video prediction.
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular.
Ranked #49 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Auxiliary Tasks in Multi-task Learning
Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation.
Evaluation of CNN-based Single-Image Depth Estimation Methods
While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited.
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks
Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals.
Building Instance Classification Using Street View Images
The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures.
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.
