## 1 Introduction
Therefore, it is necessary to design an intelligent resource scheduling algorithm to adaptively schedule multi-dimensional heterogeneous resources on the unified computing platform. On the one hand, a novel communication and computation integrated network architecture, referred to as (Com)2 INet, is proposed for intelligent scheduling of multi-dimensional resources in heterogeneous environments. Service-oriented principles and intelligence facilitate the integration of a software-defined network (SDN) [9], programming protocol independent packet processor (P4) technology [10], and network function virtualization (NFV) [11] into the designed architecture, enabling flexible network management and reduced operating costs. To be specific, SDN decouples data plane and control plane,supporting centralized control and device configuration. The combination of SDN and P4 technology allows network administrators to customize reconfigure network functions of data plane devices for supporting new functions and protocols, thereby offloading some network functions to the data plane, such as in-band telemetry [12], load balancing [13], and re-routing [14], to achieve flexible network management. Moreover, with the support of SDN and NFV technology, computing services can implement traditional network functions in the form of virtual network functions (VNFs), and can migrate from dedicated hardware devices to general-purpose devices to reduce dependence on hardware devices. In conclusion, huge computing flows can be scheduled across domains of end-edge-cloud, space-air-ground, and multiple data centers, enabling the deep integration of \"computing and network\". On the other hand, we present a case study of an intelligent scheduling scheme with dynamic coordination of multi-dimensional resources for massive remote sensing images, where a stepwise adaptive clustering approach is used to make the optimal joint decision for levee piping hazard identification. Our main contributions are summarized below. * The (Com)2 INet integrates SDN, P4, and NFV technologies. It is a service-oriented computing paradigm that can schedule multi-dimensional heterogeneous resources across domains of end-edge-cloud, space-air-ground, and multiple data centers to ensure QoS and QoE requirements of advanced services. (Com)2 INet architecture consists of three layers, i.e., a computing service layer, mapping adaption layer, and converged network layer, aiming to achieve the coordination of applications and network resources and the secure isolation of networks and applications. * We design a service-centric computing service layer with different types of service identifications. The device-centric converged network layer with storage, forwarding and computing capabilities to process the service locally or forward it to other appropriate devices. The function-centric mapping adaption layer generates orchestration strategies to guide operations of converged network layer, vertically decouples computing service layer from converged network layer, and realizes the coordinated scheduling and intelligent integration of the service level and network level. * With (Com)2 INet as support and QoS requirements as constraints, we design a stepwise adaptive clustering method for levee piping hazard identification based on temperature field of UAV thermal infrared remote sensing imagery. The method utilizes a progressive approach to iteratively eliminate background regions and refine the target area, enabling the precise localization of piping hazards. Experimental results demonstrate that the proposed method outperforms benchmarks overall performance in terms of accuracy of target extraction and computational time of the algorithm. The rest of the article is organized as follows. First, we present the (Com)2 INet architecture in the Section 2. Following that, we present an application use case, in which the experimental results are given and discussed in the Section 3. Then, we elaborate on the directions and issues for future research in the Section 4.