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With the development of information technology, the rise of surveying and mapping big data and artificial intelligence, the lack of data is no longer a problem. However, the existing surveying and mapping data processing technology has been pursuing the accuracy of data (micro), and big data research just allows the data to be mixed and uncertain (macro). Therefore, although the traditional surveying and mapping data processing theory has accumulated a large number of technical advantages in micro data processing, the large‑scale and complexity of big data has become increasingly prominent, in which traditional calculation model and analysis algorithm cannot effectively support the efficient analysis and processing of big data. As the key to the intelligent era, data processing theory and method, how to adapt to the challenges and opportunities of new technology is worth our deep thinking. Driven by big data, new ideas and methods such as large‑scale data mining, machine learning and deep learning are booming, which greatly promote the fusion of multi‑source heterogeneous big data inside and outside the scene, effectively extract surface feature information from a variety of sensor data, and constantly improve the ability of surveying and mapping information acquisition and analysis. We think that the theory of surveying and mapping data also needs to be followed up, and the existing data processing methods need to be intelligent. Combined with the frontier hot spots, development trends and existing challenges of intelligent surveying and mapping, this paper explores the expansion direction of data processing theory. One is to promote the further development of surveying data processing theory, and the other is to provide reference for graduate students who are interested in entering the field of surveying and mapping big data. [ABSTRACT FROM AUTHOR]

We address ihe problem of fine-grained parallel optimization of large-scale data. Patch-based multi-view stereo (PMVS) algorithm has been widely applied to digital city and other fields because of its good three-dimensional reconstruction effect, however, its large-scale computing algorithm has a low execution efficiency. Therefore, to address the limitation, this paper proposes a fine-grained parallel optimization method, including task allocation and load-balancing; strategies of main system memory and GPU memory? the optimization of communication. We perform CPU multi-threading operation using the pthreads function library to take full advantage of the computing power of multicore CPUs. And for GPUs, we utilize the CUDA framework while optimizing thread organization and memory access. Besides that, we propose the idea of adapting memory pool model and pipelining model to improve bandwidth availability ratio. The memory pool model reduces the impact of data resources transferring on the bus for CPUs_GPUs while waiting for resources; the pipelining model hides communication time for CPU to read data from memory. At the same time, this paper utilizes the Harris-DOG feature extraction of PMVS algorithm of sequences of images as the example to verify our optimization strategies. The experiments demonstrate that the multi-threading CPU-based strategy can achieve 4 times speed-up ratio, the highest ratio that parallel CUDA-based strategy can achieve is 34 times, and our strategy can improve the performance 30% on the basis of the parallel CUDA-based strategy. In the future, our optimization strategy can he applied to quick computing resource scheduling in big data processing of other domains. [ABSTRACT FROM AUTHOR]