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 …

Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …

Modern change detection (CD) has achieved remarkable success by the powerful
discriminative ability of deep convolutions. However, high-resolution remote sensing CD …

The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

Training deep learning-based change detection (CD) models heavily relies on large labeled
data sets. However, it is time-consuming and labor-intensive to collect large-scale …

In this paper, we implement and analyse an Attention U-Net deep network for semantic
segmentation using Sentinel-2 satellite sensor imagery, for the purpose of detecting …

Analyzing land cover changes with multitemporal remote sensing (RS) images is crucial for
environmental protection and land planning. In this article, we explore RS image change …

Change detection methods play an indispensable role in remote sensing. Some change
detection methods have obtained a fairly good performance by introducing attention …

The U-net is nowadays among the most popular deep learning algorithms for land use/land
cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar …

Understanding the dynamics of deforestation and land uses of neighboring areas is of vital
importance for the design and development of appropriate forest conservation and …