Review on Convolutional Neural Networks (CNN) in vegetation remote sensing

T Kattenborn, J Leitloff, F Schiefer, S Hinz - ISPRS journal of …, 2021‏ - Elsevier
Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …

[HTML][HTML] Transformative technologies in digital agriculture: Leveraging Internet of Things, remote sensing, and artificial intelligence for smart crop management

F Fuentes-Peñailillo, K Gutter, R Vega… - Journal of Sensor and …, 2024‏ - mdpi.com
This paper explores the potential of smart crop management based on the incorporation of
tools like digital agriculture, which considers current technological tools applied in …

Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks

R Alfred, JH Obit, CPY Chin, H Haviluddin, Y Lim - Ieee Access, 2021‏ - ieeexplore.ieee.org
Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a
large impact on Smart Farming and involve the whole supply chain, particularly for rice …

[HTML][HTML] Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications

T Hoeser, F Bachofer, C Kuenzer - Remote Sensing, 2020‏ - mdpi.com
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

Satellite image time series classification with pixel-set encoders and temporal self-attention

VSF Garnot, L Landrieu, S Giordano… - Proceedings of the …, 2020‏ - openaccess.thecvf.com
Satellite image time series, bolstered by their growing availability, are at the forefront of an
extensive effort towards automated Earth monitoring by international institutions. In …

Monitoring agriculture areas with satellite images and deep learning

TT Nguyen, TD Hoang, MT Pham, TT Vu… - Applied Soft …, 2020‏ - Elsevier
Agriculture applications rely on accurate land monitoring, especially paddy areas, for timely
food security control and support actions. However, traditional monitoring requires field …

Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia

KR Thorp, D Drajat - Remote Sensing of Environment, 2021‏ - Elsevier
Indonesia recently implemented a novel, technology-driven approach for conducting
agricultural production surveys, which involves monthly observations at many thousands of …

Map** paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data

Y Cai, H Lin, M Zhang - Advances in Space Research, 2019‏ - Elsevier
Rice is one of the world's major staple foods, especially in China. In this study, we proposed
an object-based random forest (RF) method for paddy rice map** using time series …

Large-scale rice map** under different years based on time-series Sentinel-1 images using deep semantic segmentation model

P Wei, D Chai, T Lin, C Tang, M Du, J Huang - ISPRS journal of …, 2021‏ - Elsevier
Identifying spatial distribution of crop planting in large-scale is one of the most significant
applications of remote sensing imagery. As an active remote sensing system, synthetic …

Evaluating the efficiency of coarser to finer resolution multispectral satellites in map** paddy rice fields using GEE implementation

M Waleed, M Mubeen, A Ahmad… - Scientific Reports, 2022‏ - nature.com
Timely and accurate estimation of rice-growing areas and forecasting of production can
provide crucial information for governments, planners, and decision-makers in formulating …