## 1 Introduction  
Earthmoving is a ubiquitous operation in construction projects and comprises a significant portion of the project cost and time, especially in the case of heavy civil and linear projects [1; 2; 3]. Within earthmoving operations, dump trucks are the major equipment typically used for the conveyance of material [4]. Thus, keeping track of dump truck operations, especially by counting or weighing working trucks, provides important information in the form of field earthmoving quantity statistics (FEQS). FEQS is defined as the statistical basis for the quantification of material moved in terms of quarry shipment, project site earthwork loading, or engineering waste earthwork disposal. FEQS data thus contributes to field material management and is the primary information for financial settlement with earthmoving contractors. Thus, conducting FEQS is a very important and necessary aspect of managing earthmoving operations that can help avoid practical civil engineering management problems, like earthwork smuggling, financial settlement errors, and erroneous quantity estimation. Current methods of updating FEQS rely on either manually counting the overall number of truck loads moved or on weighing trucks at load weigh stations. Both methods suffer from numerous disadvantages due to the manual nature of the former, and the high cost and disruption to operation caused in the latter, as described in the related work section. Moreover, current FEQS can be error prone or lack traceable data records, thus reducing validity. Incorrect tracking of truck counts and truck loads leads to numerous issues on construction projects between stakeholders that cause significant troubles for successful project completion. Aside from the problems of reporting inaccurate data, these issues can cause litigious situations between various stakeholders and derail the project management. On the other hand, the advent of advanced computational and artificial intelligence methods provides the possibility of automatically and objectively collecting FEQS data from site surveillance cameras, and informationized approaches make FEQS data cyber-recordable and easy to trace. This paper thus presents the framework of a novel automated, non-contact, and vision-based FEQS, with the goal of reducing manual effort, costs, and errors to collect truck load information. The core work in the FEQS framework is the full/empty-load classification of earthmoving trucks as counting full-load trucks is an effective statistics strategy under a certain scenario. Vision-based deep learning is applied in the proposed FEQS as the core work solution, and because the potential usable deep learning models are numerous, testing and selection among them is needed. Hence, a comparison study to test the deep learning models' feasibility in full/empty-load truck classification and identify in the FEQS application is developed. Through the deep learning model comparison, the core work feasibility of the proposed framework can be assessed and a practical model choice for FEQS implementation can be suggested. Thus, the main contributions of the paper can be summarized as: (1) The framework of an automated, non-contact FEQS applying vision-based deep learning is presented, which has advantages toward existed FEQS methods in terms of manual effort, costs, and errors; (2) the core work of the framework, i.e., the classification of full/empty-load trucks in earthmoving operations, is assessed in terms of feasibility through a comparison study that involves multiple deep learning models; and (3) the comparison study results are further discussed to give model choice suggestions for future implementation of the proposed FEQS. The rest of the paper is as follows: Related work of the current state of art and practice in the domain of earthmoving FEQS and vison-based deep learning is first reviewed to identify the gaps in knowledge and thus to explain the proposed FEQS framework and the authors' research.