This paper presents the developments achieved via the Social Cooperation Program "Intelligent Construction System," from three primary perspectives: environmental measurements, improvements in remote operability, and improvements in efficiency and automation of remote operation. For improvements in remote operation, environmental measurements of the disaster sites are critical. Therefore, a method to integrate the data from drones and ground-based vehicles in order to generate 3D mapswas proposed. Another method for estimating the changes in soil volumes through a 3D map based on drone data was also proposed. Finally, to estimate the trafficability in disaster sites, a cone index-based method employing spectral images was proposed. Improving remote operability is essential to facilitate improved working conditions for operators. Considering this, a method providing human operators with a bird's-eye view of remotely operated machinery from any perspective was proposed. Additionally, to avoid the tumbling of remotely operated machinery, a running stability presentation method was proposed; this method presented the human operator with a tumble risk index. For improving efficiency and automation, an automatic camera control method, based on requirements of construction machine operators, was proposed. Using this method, the need for a dedicated human camera operator could be bypassed. Furthermore, for the automatic measurement of construction time and content, a method based on deep learning and using cameras for recognizing the actions of construction machinery was proposed. Preliminary experiments on some of the proposed methods in real environments yielded promising results.