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Yao, Y.; Wang, X.; Luo, L.; Wan, H.; Ren, H. Merits and Challenges in Geographic-Information-Systems and Remote-Sensing Application. Encyclopedia. Available online: https://encyclopedia.pub/entry/53371 (accessed on 02 July 2024).
Yao Y, Wang X, Luo L, Wan H, Ren H. Merits and Challenges in Geographic-Information-Systems and Remote-Sensing Application. Encyclopedia. Available at: https://encyclopedia.pub/entry/53371. Accessed July 02, 2024.
Yao, Ya, Xinyuan Wang, Lei Luo, Hong Wan, Hongge Ren. "Merits and Challenges in Geographic-Information-Systems and Remote-Sensing Application" Encyclopedia, https://encyclopedia.pub/entry/53371 (accessed July 02, 2024).
Yao, Y., Wang, X., Luo, L., Wan, H., & Ren, H. (2024, January 03). Merits and Challenges in Geographic-Information-Systems and Remote-Sensing Application. In Encyclopedia. https://encyclopedia.pub/entry/53371
Yao, Ya, et al. "Merits and Challenges in Geographic-Information-Systems and Remote-Sensing Application." Encyclopedia. Web. 03 January, 2024.
Merits and Challenges in Geographic-Information-Systems and Remote-Sensing Application
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This entry is adapted from the peer-reviewed paper 10.3390/rs15245766

Geographic Information Systems (GIS) and remote sensing (RS) have evolved into distinct disciplines within geospatial technology, each with well-established theoretical foundations and methodologies. They now encompass various methodologies and software tools(ArcGIS Pro, GRASS GIS, QGIS, ENVI, ERDAS Imagine etc.), expanding their applications in spatial data collection, measurement, analysis, storage, management, display, dissemination, and deployment. GIS has moved beyond just creating digital maps. It has evolved into a comprehensive framework for integrating, storing, analyzing, and presenting geospatial data. RS involves observational and investigative activities in the environmental realm. By continuously monitoring the surface environment, GIS and RS integrate temporal and spatial dimensions, enhancing our understanding of the natural world and the human–nature relationship. This understanding is crucial for recognizing, managing, and preserving archaeological and cultural heritage (ACH).

GIS RS ACH merits investigation data challenges

1. Introduction

In the 1870s, Richthofen introduced the concept of the Silk Road (SR) in his publication “China” [1]; the term “Silk Road” refers to the trade routes extending from Luoyang-Chang’an to Samarkand that primarily facilitated the exchange of spices and silk. Subsequently, the term “Silk Road” swiftly gained recognition within academic circles and the public sphere, extending its scope to encompass both the Maritime Silk Road and the Grassland Silk Road [1][2]. It is widely accepted that the SR involved the Maritime Silk Road and the Land Silk Road, spanning from the 2nd century BCE to the 16th century BC. In 2014, a collaborative declaration by China, Kazakhstan, and Kyrgyzstan for the “Silk Road: The Road Network of Chang’an-Tianshan Corridor” was successfully selected into the World Heritage List. As a landmark event for SR cultural heritage, this declaration revitalized global awareness of the profound historical significance inherent in the SR. As a system of caravan routes connecting Eurasia and North Africa, the SR promotes the mutual dissemination of science and technology, cultural exchange, and integration of people in the East and the West, which has extensively and profoundly promoted production progress and even social change in countries along the routes [3].
However, serving as the carrier of history and culture, which bears witness to the cultural interaction that took place in or around them, cultural heritage sites along the SR are suffering from human activities and climate change [4][5][6][7]. Limited by traditional preventive conservation techniques and methods, the safeguarding and management of these invaluable sites face unprecedented challenges [8]. Within the myriad of challenges, three key aspects have garnered significant attention in recent scholarly discourse. Specifically, the bottleneck of traditional survey methods hinders the overall acquisition and observation of large-scale heritage information [9][10][11]. The ACH sites along the routes are highly susceptible to atmospheric changes [4][12], weathering [13][14][15][16], erosion, and human activities [14][17][18][19][20], which underscores the urgent need for risk assessment and monitoring to facilitate the preventive conservation of ACH. While several research projects involving Central Asian Archaeological Landscapes [21] and the Digital Silk Road Project [22] are currently making commendable contributions and exerting substantial efforts towards the Silk Road Digital Inventory [21], it is essential to acknowledge that, given the extensive geographical coverage of the Silk Road and the sheer abundance of heritage sites, the development of comprehensive digital documentation remains notably incomplete.
In recent years, an increasing number of peer-reviewed articles have demonstrated the application of remote sensing (RS) and Geographic Information Systems (GIS) in tackling various challenges related to cultural heritage, encompassing the ones outlined above and beyond [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. Notably, RS and GIS have emerged as tools in numerous cultural heritage research and management [29][45][46][47][48][49][50][51][52][53]. The integration of RS and GIS in cultural heritage studies represents a blend of conventional yet innovative approaches [54][55][56]. These tools are expanding the practice of SR ACH conservation [57][58][59][60][61][62][63]. As space technology continues to advance, RS and GIS are evolving to keep pace with the era of data proliferation. To furnish technical support for ACH research and its sustainable conservation, it is imperative to continuously review trends in cultural heritage research. The Silk Road, with its vast geographical expanse and rich, diverse cultural heritage, offers an excellent repository of case studies for assessing the trends of these technologies.

2. Merits of GIS-RS Applications for ACH

2.1. High Efficiency for ACH Investigation

The low cost and high efficiency of cultural heritage information acquisition based on GIS-RS are realized. RS offers a fast, convenient, and labor-saving method for the detection of ACH, especially during the large-scale performance of archaeological land surveys. Conventional archaeology predominantly relies on manual site investigations, particularly for extensive site surveys, which entail substantial labor efforts. Particularly, when conducting investigations in challenging natural environments such as deserts, grasslands, and ancient city sites, the inherent limitations of these settings render field investigations arduous, further complicating the attainment of precise survey results. In contrast, remote sensing platforms gather data without being constrained by geographical environments, leading to substantial time and cost savings in archaeological investigations.
Moreover, in the realm of heritage risk assessment, particularly concerning ancient buildings, ICCROM underscores the significance of minimal intervention and investment to attain efficiency [64]. Remote sensing-GIS techniques align with this philosophy by employing non-invasive monitoring methods, embodying the efficient principles and concepts of risk management. Furthermore, the applied analysis of multi-temporal data enables managers to swiftly identify potential sites or changes, thereby contributing to time and resource conservation.

2.2. Quality Improvement for ACH Digital Source

The quality of digital resources pertaining to cultural heritage has been enhanced. The management, protection, and research of cultural heritage impose stringent demands for the integrity, consistency, objectivity, and precision of foundational data. A comprehensive cultural heritage database comprises heritage ontology information along with environmental background data. Satellite remote sensing technology offers macroscopic, rapid, dynamic, and cost-effective capabilities, facilitating all-weather and continuous monitoring of diverse surface conditions.
The utilization of multiple platforms and data collection cycles substantially enhances the integrity of cultural heritage data. Spatial alignment of data from multiple sources guarantees data consistency. The integration of machine learning mitigates subjectivity in human–computer interaction processes. Enhanced data and model accuracy facilitate more precise monitoring of cultural heritage. These factors collectively contribute to a high-quality cultural heritage data resource for research and management.

2.3. Cognitive Enhancement for ACH Research

Augment the comprehensive comprehension of cultural heritage within expansive spatial contexts. GIS-RS commands powerful abilities in ACH information mining. Spatial analysis can identify and explain the economic, environmental, and social impacts of the ACH layout and related land-use patterns for historical or cultural researchers. This can further help scholars recognize and understand the interactions between the environment and human activities in the ancient economic evolution and the complexity of social organization changes.
GIS-RS methods can achieve both static and dynamic temporal and spatial changes. The addition of the environmental background further makes it possible for scholars to explore complex environmental drivers or social forces. Specialists can extract key work areas of ACH via GIS-RS, which can also provide scientific data support for early warning mechanisms apropos natural and human-created cultural heritage threats and can assist in the amelioration of monitoring and response measures pertaining to ACH [65]. GIS-RS can also offer analytical support for the development and management of ACH tourism: its planning at the initial stage of development, spatial analysis research on the accessibility of cultural resources, the rationality of transportation, and flow control at heritage sites.

3. Challenges of GIS-RS in ACH Application

3.1. Heterogeneous Data Problem

Heterogeneous data in multi-source data always remains a challenge. While addressing data heterogeneity remains a fundamental task in ACH research employing GIS and RS, it presents limitations in detail that are not necessarily difficult but cannot be ignored. The swift advancements in remote sensing earth observation technology and computer technology have led to the rapid development of multi-spectral, high-spatial resolution remote sensing data sharing platforms, generating vast quantities of remote sensing data daily and resulting in explosive data volume growth. This growth, in turn, introduces multi-sourced data and data heterogeneity. Furthermore, Silk Road cultural heritage data frequently originate from diverse sources and modalities, exhibiting variations in language composition, platform architecture, and document structure [66]. These disparities in data formats underscore the characteristics of multi-sourced heterogeneity, posing significant challenges to data processing efficiency and comprehensiveness. Distinct remote sensing platforms, including satellite remote sensing and low-altitude remote sensing, as well as variations in satellite data, necessitate distinct pre-processing methods. Additionally, geographic vector data referenced in different coordinate systems and textual information presented in various languages further complicate unified data storage.

3.2. Association and Correlation in ACH Data Mining

Establishing correlation levels in data mining challenges the attribution of ACH research. The challenge in establishing a cultural heritage database within the context of GIS and RS applications does not primarily stem from the volume or complexity of “big data”. Rather, it pertains to the nuanced development of data significance and value gradients, necessitating the identification and selection of pertinent data [67][68]. Confronted with intricate geographical environments, the quantitative analysis of cultural heritage frequently overlooks the establishment of correlation levels. This encompasses correlations between cultural heritage and its environmental context, spatial and temporal relationships, cultural elements, and public engagement. This omission may be attributed to the multifaceted and intricate factors influencing changes in cultural heritage. Analyzing how each of these causes affects heritage and how they affect it collectively is extremely complex. Nevertheless, it results in a deficiency in attributing ailments afflicting cultural heritage. For instance, while deformation is detected in Angkor site monitoring, comprehensive causal analyses of these deformations are frequently absent, hindering determinations regarding whether tourism, urban development, extreme climate events, or other factors constitute primary contributors.

3.3. Interdisciplinary Dilemma

Additionally, cross-application encounters cognitive limitations stemming from interdisciplinary disparities. GIS-RS presents theoretical and methodological challenges for ACH managers. Effective GIS-RS applications require a thorough knowledge of the theoretical and methodological limitations inherent in the technology as well as the awareness of their implications for the modeling of ACH data. However, most specialists, regardless of whether they are historical and cultural researchers or cultural heritage managers, have not systematically studied the theory and tools of GIS-RS. This lack of expertise can directly lead to short applications of GIS-RS, such as the abuse of spatial analysis models in archaeological analysis [69]. A similar challenge arises in risk monitoring. For instance, despite conducting meticulous remote sensing-based deformation monitoring of Angkor Wat, researchers encounter difficulties in assessing the risk level associated with deformation-related issues. Profound insights from experts specializing in ancient building preservation are essential for providing professional guidance, a task not within the purview of cultural heritage experts. This challenge also extends to issues such as model accuracy evaluation and confidence level selection for skilled archaeologists or Cultural Heritage Specialists.

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Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Ya Yao
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Xinyuan Wang
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Hong Wan
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Hongge Ren
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