Papers Crop Type Mapping
“Crop Type Mapping” 태그가 달린 논문 20편 · 필터 해제
Model-Agnostic, Temperature-Informed Sampling Enhances Cross-Year Crop Mapping with Deep Learning
Conventional benchmarks for crop type classification from optical satellite time series typically assume access to labeled data from the same year and rely on fixed calendar-day sampling. This limits generalization acros…
Crop Type MappingTime SeriesUncertainty QuantificationOn the Generalizability of Foundation Models for Crop Type Mapping
Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text generation, and image recognition. The E…
Crop ClassificationCrop Type MappingDisaster ResponseEarth Observation+4SpectralEarth: Training Hyperspectral Foundation Models at Scale
Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potential in hyperspectral imaging (HSI) remai…
Computational EfficiencyCrop Type MappingSelf-Supervised LearningEvaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI
When we are primarily interested in solving several problems jointly with a given prescribed high performance accuracy for each target application, then Foundation Models should for most cases be used rather than problem…
BenchmarkingCrop Type MappingDensity EstimationEarth Observation+1Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach
Accurate and timely crop mapping is essential for yield estimation, insurance claims, and conservation efforts. Over the years, many successful machine learning models for crop mapping have been developed that use just t…
Crop Type MappingSICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters
The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, whi…
Crop Type MappingCrop Yield PredictionEarth ObservationHarvesting Date Prediction+2Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
Climate change is increasingly disrupting worldwide agriculture, making global food production less reliable. To tackle the growing challenges in feeding the planet, cutting-edge management strategies, such as precision …
Crop ClassificationCrop Type MappingImage SegmentationSemantic SegmentationRevolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions
Food security, a global concern, necessitates precise and diverse data-driven solutions to address its multifaceted challenges. This paper explores the integration of AI foundation models across various food security app…
Crop Type MappingCrop Yield PredictionDecision MakingLearning crop type mapping from regional label proportions in large-scale SAR and optical imagery
The application of deep learning algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given the data scale in EO, creating large data…
Contrastive LearningCrop Type MappingDeep ClusteringEarth Observation+1Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution
Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizens. In the last decade, the amount of ava…
Crop Type MappingData AugmentationEarth ObservationLanguage Modelling+2Tampered VAE for Improved Satellite Image Time Series Classification
The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising fro…
ClassificationCrop ClassificationCrop Type MappingGPU+3Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach
Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower the expensive cost associated with coll…
Crop ClassificationCrop Type MappingLand Cover ClassificationManagementCrop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated sate…
ClassificationCrop ClassificationCrop Type MappingTime Series+1Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops
High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these…
Crop Type MappingTwo Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions
Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type ma…
Crop Type MappingVocal Bursts Type PredictionFrom parcel to continental scale -- A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations
Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to mon…
Crop Type MappingSemantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods
Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing m…
Crop Type MappingFlood extent forecastingSemantic SegmentationBreizhCrops: 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. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere tim…
Crop Type MappingTime SeriesTime Series AnalysisVocal Bursts Type PredictionEnd-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions …
ClassificationCrop ClassificationCrop Type MappingEarly Classification+4Edge Detection in Radar Images Using Weibull Distribution
Radar images can reveal information about the shape of the surface terrain as well as its physical and biophysical properties. Radar images have long been used in geological studies to map structural features that are re…
Crop Type MappingEdge Detection