Although deep learning techniques have brought unprecedented accuracy to automatic
building extraction, several main issues still constitute an obstacle to effective and practical …

In the last two decades, we have witnessed the intensive development of artificial
intelligence in the field of agriculture. In this period, the transition from the application of …

Advances in remote sensing image processing techniques have further increased the
demand for annotated datasets. However, preparing annotated multi-temporal 2D/3D …

Deep Learning (DL) has a wide variety of applications in various thematic domains,
including spatial information. Although with limitations, it is also starting to be considered in …

In the process of coal mining, a certain amount of gas will be produced. Environmental
perception is very important to realize intelligent and unmanned coal mine production and …

Enabled by advancing technology, coral reef researchers increasingly prefer use of image-
based surveys over approaches depending solely upon in situ observations, interpretations …

A deep neural network (DNN), evolved from a traditional artificial neural network, has been
seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has …

The paper presents a simple, yet robust computer vision system for robot arm tracking with
the use of RGB-D cameras. Tracking means to measure in real time the robot state given by …

In this article, we propose a multimodal co-learning framework for building change detection.
This framework can be adopted to jointly train a Siamese bitemporal image network and a …

In this paper, we aim to improve the transfer learning ability of 2D convolutional neural
networks (CNNs) for building extraction from optical imagery and digital surface models …