Towards interpreting multi-temporal deep learning models in crop mapping
Multi-temporal deep learning approaches have exhibited excellent classification
performance in large-scale crop mapping. These approaches efficiently and automatically …
performance in large-scale crop mapping. These approaches efficiently and automatically …
Deep learning for land cover change detection
Land cover and its change are crucial for many environmental applications. This study
focuses on the land cover classification and change detection with multitemporal and …
focuses on the land cover classification and change detection with multitemporal and …
A sentinel-2 multiyear, multicountry benchmark dataset for crop classification and segmentation with deep learning
In this work, we introduce Sen4AgriNet, a Sentinel-2-based time series multicountry
benchmark dataset, tailored for agricultural monitoring applications with machine and deep …
benchmark dataset, tailored for agricultural monitoring applications with machine and deep …
Urban overheating assessment through prediction of surface temperatures: A case study of karachi, Pakistan
Global climate has been radically affected by the urbanization process in recent years.
Karachi, Pakistan's economic hub, is also showing signs of swift urbanization. Owing to the …
Karachi, Pakistan's economic hub, is also showing signs of swift urbanization. Owing to the …
Semantic segmentation based on temporal features: Learning of temporal–spatial information from time-series SAR images for paddy rice mapping
Synthetic aperture radar (SAR) can be used to obtain remote sensing images of different
growth stages of crops under all weather conditions. Such time-series SAR images can …
growth stages of crops under all weather conditions. Such time-series SAR images can …
Mapping integrated crop-livestock systems in Brazil with planetscope time series and deep learning
Accurate mapping of crops with high spatiotemporal resolution plays a critical role in
achieving the Sustainable Development Goals (SDGs), especially in the context of …
achieving the Sustainable Development Goals (SDGs), especially in the context of …
Deep Learning for Satellite Image Time-Series Analysis: A review
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 …
state of Earth and its land cover for over 50 years. Long-term missions, such as those of …
Extraction of Tobacco Planting Information Based on UAV High-Resolution Remote Sensing Images
L He, K Liao, Y Li, B Li, J Zhang, Y Wang, L Lu, S Jian… - Remote Sensing, 2024 - mdpi.com
Tobacco is a critical cash crop in China, so its growing status has received more and more
attention. How to acquire accurate plant area, row spacing, and plant spacing at the same …
attention. How to acquire accurate plant area, row spacing, and plant spacing at the same …
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery
LEC La Rosa, DAB Oliveira… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The application of deep learning (DL) algorithms to Earth observation (EO) in recent years
has enabled substantial progress in fields that rely on remotely sensed data. However, given …
has enabled substantial progress in fields that rely on remotely sensed data. However, given …
Time-series prediction of onion quality changes in cold storage based on long short-term memory networks
This study presents a recurrent neural network (RNN)-based model for predicting physical
quality changes in onions during long-term low-temperature storage. Unlike previous …
quality changes in onions during long-term low-temperature storage. Unlike previous …