Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …

Building detection from very high resolution (VHR) optical remote sensing images, which is
an essential but challenging task in remote sensing, has attracted increased attention in …

Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …

One of the most challenging research subjects in remote sensing is feature extraction, such
as road features, from remote sensing images. Such an extraction influences multiple …

Benchmark datasets are critical for develo**, evaluating, and comparing remote sensing
image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in …

The small object semantic segmentation task is aimed at automatically extracting key objects
from high-resolution remote sensing (HRS) imagery. Compared with the large-scale …

Agricultural greenhouses (AGs) are an important facility for the development of modern
agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning …

Extracting roads from satellite imagery is a promising approach to update the dynamic
changes of road networks efficiently and timely. However, it is challenging due to the …