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Found 28 papers, 15 papers with code
Mixture of Experts with Uncertainty Voting for Imbalanced Deep Regression Problems
If training data are imbalanced, the learning is dominated by the densely covered regions of the target distribution, consequently, the learned regressor tends to exhibit poor performance in sparsely covered regions.
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface.
Ranked #1 on
Cloud Removal
on SEN12MS-CR-TS
An evaluation of deep learning models for predicting water depth evolution in urban floods
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution.
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
A high-resolution canopy height model of the Earth
The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity.
A Deep Learning Approach for Digital Color Reconstruction of Lenticular Films
We validate the proposed method on a lenticular film dataset and compare it to other approaches.
Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles
To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR
The increasing demand for commodities is leading to changes in land use worldwide.
Towards a Collective Agenda on AI for Earth Science Data Analysis
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer.
In the light of feature distributions: moment matching for Neural Style Transfer
Style transfer aims to render the content of a given image in the graphical/artistic style of another image.
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle.
PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds
Next, the corners are linked with an exhaustive set of candidate edges, which is again pruned to obtain the final wireframe.
Crop mapping from image time series: deep learning with multi-scale label hierarchies
The three-level label hierarchy is encoded in a convolutional, recurrent neural network (convRNN), such that for each pixel the model predicts three labels at different level of granularity.
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
Automated semantic segmentation and object detection are of great importance in geospatial data analysis.
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.
Deep Active Learning in Remote Sensing for data efficient Change Detection
We investigate active learning in the context of deep neural network models for change detection and map updating.
Privileged Pooling: Better Sample Efficiency Through Supervised Attention
We propose a scheme for supervised image classification that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets.
Geocoding of trees from street addresses and street-level images
We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching.
HistoNet: Predicting size histograms of object instances
Our method results in an overall improvement in the count and size distribution prediction as compared to state-of-the-art instance segmentation method Mask R-CNN.
Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients.
From Google Maps to a Fine-Grained Catalog of Street trees
We believe this is the first work to exploit publicly available image data for fine-grained tree mapping at city-scale, respectively over many thousands of trees.
Country-wide high-resolution vegetation height mapping with Sentinel-2
Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland.
Guided Super-Resolution as Pixel-to-Pixel Transformation
Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e. g., perspective depth acquired with a time-of-flight camera) and a high-resolution guide image from a different domain (e. g., a grey-scale image from a conventional camera); and the target output is a high-resolution version of the source (in our example, a high-res depth map).
Topological Map Extraction from Overhead Images
We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly.
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data
Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field.
Learning Aerial Image Segmentation from Online Maps
We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled, pixel-accurate ground truth of the same city to automatic training data derived from OpenStreetMap data from distant locations.
Towards seamless multi-view scene analysis from satellite to street-level
In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.
