[HTML][HTML] Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery

Z Han, C Zhang, L Gao, Z Zeng, B Zhang… - ISPRS Journal of …, 2023 - Elsevier
Accurate crop mapping is of great significance for crop yield forecasting, agricultural
productivity development and agricultural management. Thanks to its all-time and all …

Location-aware adaptive normalization: a deep learning approach for wildfire danger forecasting

MHS Eddin, R Roscher, J Gall - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Climate change is expected to intensify and increase extreme events in the weather cycle.
Since this has a significant impact on various sectors of our life, recent works are concerned …

[HTML][HTML] Classifying raw irregular time series (CRIT) for large area land cover mapping by adapting transformer model

HK Zhang, D Luo, Z Li - Science of Remote Sensing, 2024 - Elsevier
For Landsat land cover classification, the time series observations are typically irregular in
the number of observations in a period (eg, a year) and acquisition dates due to cloud cover …

Benefits of transformer: In-context learning in linear regression tasks with unstructured data

Y Xing, X Lin, N Suh, Q Song, G Cheng - arXiv preprint arXiv:2402.00743, 2024 - arxiv.org
In practice, it is observed that transformer-based models can learn concepts in context in the
inference stage. While existing literature, eg,\citet {zhang2023trained, huang2023context} …

Deep Learning for Satellite Image Time-Series Analysis: A review

L Miller, C Pelletier, GI Webb - IEEE Geoscience and Remote …, 2024 - ieeexplore.ieee.org
Earth observation (EO) satellite missions have been providing detailed images about the
state of Earth and its land cover for over 50 years. Long-term missions, such as those of …

[HTML][HTML] Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data

V Barriere, M Claverie, M Schneider, G Lemoine… - Remote Sensing of …, 2024 - Elsevier
Accurate early-season crop type classification is crucial for the crop production estimation
and monitoring of agricultural parcels. However, the complexity of the plant growth patterns …

Reconstruction of seamless harmonized Landsat Sentinel-2 (HLS) time series via self-supervised learning

H Liu, HK Zhang, B Huang, L Yan, KK Tran… - Remote Sensing of …, 2024 - Elsevier
Abstract The Harmonized Landsat Sentinel-2 (HLS) data, harmonizing Landsat-8/9 and
Sentinel-2 imagery, offers frequent 30 m resolution multispectral observations but is often …

Self-supervised learning--A way to minimize time and effort for precision agriculture?

ML Marszalek, BL Saux, PP Mathieu… - arXiv preprint arXiv …, 2022 - arxiv.org
Machine learning, satellites or local sensors are key factors for a sustainable and resource-
saving optimisation of agriculture and proved its values for the management of agricultural …

[HTML][HTML] Evaluating the spatial–temporal transferability of models for agricultural land cover mapping using Landsat archive

J Wijesingha, I Dzene, M Wachendorf - ISPRS Journal of Photogrammetry …, 2024 - Elsevier
Abstract Changes in policy and new plans can significantly influence land use and trigger
land use change in the long term. The data for pre-and post-policy implementation is …

[HTML][HTML] Detection and attribution of cereal yield losses using Sentinel-2 and weather data: A case study in South Australia

K Duan, A Vrieling, M Schlund, UB Nidumolu… - ISPRS Journal of …, 2024 - Elsevier
Weather extremes affect crop production. Remote sensing can help to detect crop damage
and estimate lost yield due to weather extremes over large spatial extents. We propose a …