# TPRoGI: a comprehensive rock glacier inventory for the Tibetan Plateau using deep learning

Zhangyu Sun, Yan Hu, Adina Racoviteanu, Lin Liu, Stephan Harrison, Xiaowen Wang, Jiaxin Cai, Xin Guo, Yujun He, Hailun Yuan

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Zhangyu Sun, Yan Hu, Adina Racoviteanu, Lin Liu, Stephan Harrison, et al.. TPRoGI: a comprehensive rock glacier inventory for the Tibetan Plateau using deep learning. Earth System Science Data, 2024, 16 (12), pp.5703-5721. 10.5194/essd-16-5703-2024. hal-04950570

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# TPRoGI: a comprehensive rock glacier inventory for the
Tibetan Plateau using deep learning

Zhangyu Sun¹, Yan Hu¹,², Adina Racoviteanu³, Lin Liu¹,², Stephan Harrison⁴, Xiaowen Wang⁵,
Jiaxin Cai⁵, Xin Guo⁵, Yujun He⁵, and Hailun Yuan⁵

¹Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China

²Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong,
Hong Kong, China

³Université Grenoble Alpes, CNRS, IRD, IGE, Saint-Martin-d'Hères, France

⁴Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom

⁵Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China

**Correspondence:** Yan Hu (huyan@link.cuhk.edu.hk)

Received: 27 January 2024 – Discussion started: 4 March 2024

Revised: 9 October 2024 – Accepted: 20 October 2024 – Published: 17 December 2024

**Abstract.** Rock glaciers – periglacial landforms commonly found in high-mountain systems – are of significant scientific value for inferring the presence of permafrost, understanding mountain hydrology, and assessing climate impacts on high-mountain environments. However, inventories remain patchy in many alpine regions, and as a result they are poorly understood for some areas of High Mountain Asia such as the Tibetan Plateau. To address this gap, we compiled a comprehensive inventory of rock glaciers for the Tibetan Plateau, i.e., TPRoGI (v1.0), developed using an innovative deep learning method. This inventory consists of a total of 44 273 rock glaciers, covering approximately 6000 km², with a mean area of 0.14 km². They are predominantly situated at elevations ranging from 4000 to 5500 m a.s.l., with a mean of 4729 m a.s.l. They tend to occur on slopes with gradients between 10 and 25°, with a mean of 17.7°. Across the plateau, rock glaciers are widespread in the northwestern and southeastern areas, with dense concentrations in the Western Pamir and Nyainqêntanglha, while they are sparsely distributed in the inner part. Our inventory serves as a benchmark dataset, which will be further maintained and updated in the future. This dataset constitutes a significant contribution towards understanding, future monitoring, and assessment of permafrost on the Tibetan Plateau in the context of climate change. The dataset is available at https://doi.org/10.5281/zenodo.10732042 (Sun et al., 2024a).

## 1 Introduction

The Tibetan Plateau, the highest and largest plateau on Earth, is experiencing more pronounced warming than the global average. Currently, the warming rate on the plateau is 0.031 °C yr⁻¹, higher than the global rate of 0.014 °C yr⁻¹ since the 1960s (Zhang et al., 2020). Moreover, areas underlain by permafrost on the plateau have experienced an even higher warming rate of 0.05 °C yr⁻¹ since 2004 (Zhao and Sheng, 2019). This accelerated warming trend has led to rapid degradation of permafrost, which is manifested

as increasing ground temperature, decreasing permafrost area, thickening active layer, and increasing occurrence of thermokarst lakes and thaw slumps (Zhao et al., 2020; Mu et al., 2020). A valuable indicator of permafrost comes in the form of rock glaciers, defined as “debris landforms generated by the former or current creep of frozen ground (permafrost), detectable in the landscape with the following morphologies: front, lateral margins, and optionally ridge-and-furrow surface topography” (RGIK, 2023). These landforms are widespread across the plateau, especially in the mountainous regions.
Understanding of rock glaciers within the scientific com-
munity has been evolving since the publication of the initial
article by Spencer (1900) on "a peculiar form of talus". Over
the past century, the identification of rock glaciers has been
the subject of ongoing debate, and the criteria for identify-
ing them have evolved with an increasing number of studies
worldwide (Capps, 1910; Barsch, 1996; Haeberli et al., 2006;
Berthling, 2011; Jones et al., 2019b; Janke and Bolch, 2021).
In the last decade, the identification and compilation of rock
glacier inventories has sparked heated debate due to the in-
tricate nature of these landforms (Berthling, 2011; Brardi-
noni et al., 2019). In response to the challenge posed by in-
consistencies in the identification and compilation of rock
glaciers, the International Permafrost Association (IPA) Ac-
tion Group Rock Glacier Inventories and Kinematics (RGIK)
was established in 2018 with the aim of developing widely
accepted guidelines for rock glacier inventorying, thereby
fostering a globally consistent and comprehensive approach
to rock glacier inventories (Delaloye et al., 2018; RGIK,
2023). Through the efforts of RGIK, the baseline and prac-
tical guidelines have been documented and updated in sev-
eral versions, which greatly promote the global assemblage
and uniform completion of rock glacier inventories (RGIK,
2023). This paper closely follows the RGIK guidelines in the
conceptual definition of rock glaciers.

Rock glaciers are important to map and monitor for sev-
eral reasons. First, they serve as visible indicators of frozen
ground and provide essential information about the pres-
ence and extent of mountain permafrost (Barsch, 1996; Hae-
berli, 2006). Therefore, they are valuable for assessing the
permafrost distribution (Boeckli et al., 2012; Schmid et al.,
2015; Hassan et al., 2021; Li et al., 2024). Previous stud-
ies have used rock glacier inventories to construct permafrost
maps in different regions. For instance, Boeckli et al. (2012)
developed the Alpine Permafrost Index Map for the Eu-
ropean Alps by calibrating a statistical model with rock
glacier inventories. Schmid et al. (2015) used rock glaciers
mapped from Google Earth to validate permafrost maps in
the Hindu Kush Himalaya region. Similarly, Hassan et al.
(2021) and Li et al. (2024) used rock glacier inventories to
model the permafrost probability distribution in their study
areas. Second, rock glaciers are an integral component of
mountain hydrological system, especially in arid regions
(Corte, 1976; Azócar and Brenning, 2010; Rangcroft et al.,
2013, 2015; Munroe, 2018), which is a potentially signifi-
cant water resource that remains poorly quantified. Jones et
al. (2021a) estimated that 62.02 ± 12.40 Gt of water volume
equivalent (WVEQ) is stored within rock glaciers globally.
The ratio of rock-glacier-to-glacier WVEQ was estimated to
be 1 : 618, which is expected to further increase with the on-
going melting of glaciers (Jones et al., 2021a). Given the arid
conditions of much of the western Tibetan Plateau, the inven-
tory of rock glaciers is critical for assessing potential water
resources in these regions. Third, the kinematic behavior of
rock glaciers is sensitive to changes in permafrost temper-

ature and pore-water pressure, which are influenced by cli-
mate forcing such as air temperature and precipitation (e.g.,
Arenson et al., 2002; Arenson and Springman, 2005; Ci-
coira et al., 2019a, b). Numerous studies have demonstrated
a decadal to multi-decadal acceleration trend in rock glacier
velocity in many regions such as the European Alps (e.g.,
Delaloye et al., 2010; Marcer et al., 2021), northern Tien
Shan (Kääb et al., 2021), and the Andes (Vivero et al., 2021).
Based on these global trends, rock glacier velocity (RGV)
has been added as a new product of the essential climate
variable (ECV) permafrost by the Global Climate Observ-
ing System (Zemp et al., 2022). Fourth, rapid movement or
destabilization of rock glaciers can trigger geohazards such
as rockfalls, debris flows, and lake outbursts, posing a poten-
tial risk to nearby human infrastructure and facilities (Janke
and Bolch, 2021; Marcer et al., 2021).

A full understanding of the role of rock glaciers on per-
mafrost distribution, mountain hydrology, and hazards in re-
gions such as the Tibetan Plateau is currently hampered by
a lack of comprehensive and systematic inventories. Compil-
ing a comprehensive inventory constitutes the first step to-
wards monitoring the long-term evolution of rock glaciers
and understanding the changes of mountain permafrost un-
der climate change. In recent years, rock glacier inventories
in several local areas on the Tibetan Plateau have been es-
tablished by visually interpreting optical images of different
sources, and in some cases, interferometric synthetic aperture
radar (InSAR) maps were used (Jones et al., 2018, 2021b;
Ran and Liu, 2018; Hassan et al., 2021; Reinosch et al., 2021;
Cai et al., 2021; Zhang et al., 2021, 2022, 2023; Bolch et al.,
2022; Hu et al., 2023, 2024; Li et al., 2024) (see Table S1
in the Supplement). However, the coverage remains patchy,
and a plateau-wide open-access inventory compiled from a
consistent set of images using a systematic methodology is
currently still lacking – hence the purpose of this study.

However, the production of a rock glacier inventory
through visual interpretation requires strong geomorpholog-
ical expertise and is labor-intensive and time-consuming
(Barsch, 1996; RGIK, 2023). Rock glaciers exhibit spectral
properties similar to their surrounding environment, mak-
ing it challenging to identify in optical remote sensing im-
ages (Robson et al., 2020). Moreover, in high-mountain en-
vironments, there are various landforms that resemble rock
glaciers, such as debris-covered glaciers, rock avalanches,
debris flows, and fluvial landforms (Haeberli et al., 2006;
Robson et al., 2020). As a result, inexperienced analysts are
prone to making erroneous judgments. With the development
of artificial intelligence, deep learning models have become
valuable tools for mapping complex landforms such as rock
glaciers. Deep learning models are able to learn the visual
patterns of objects and to identify features in previously un-
seen images with high accuracy (LeCun et al., 2015; Huang
et al., 2020). In recent years, several studies have successfully
employed deep learning techniques for the automatic detec-
tion of rock glaciers, yielding satisfactory results (Feng et al.,
2019; Robson et al., 2020; Marcer, 2020; Xu et al., 2021; Er-harter et al., 2022; Hu et al., 2023). However, the methods employed in previous studies are not systematic over large areas, leading to inconsistencies and patchy coverage.

In this study, we created the most extensive plateau-wide inventory of rock glaciers on the Tibetan Plateau, i.e., TPRoGI (v1.0), using a deep learning method based on the DeepLabv3+ model. It is expected that the benchmark dataset produced by this study will be maintained and updated in the future and will facilitate investigations into many scientific questions related to rock glaciers and mountain permafrost on the Tibetan Plateau.

## 2 Study area

The Tibetan Plateau is part of High Mountain Asia, covering an area of approximately $2.5 \times 10^6$ km² with an average elevation of over 4500 m above sea level (Royden et al., 2008). The Tibetan Plateau has a continental climate dominated by the Indian monsoon, the East Asian monsoon, and the westerlies. The monsoon brings warm and moist air in summer, while the westerlies bring dry and cold air in winter. The interaction between monsoons and westerlies causes distinct seasonal climate variations and significant diurnal temperature differences on the Tibetan Plateau (Yao et al., 2012). Due to its high altitude and extreme weather conditions, the Tibetan Plateau has the largest cryosphere extent outside the Arctic and Antarctic regions and the largest area of permafrost terrain in the mid- and low-latitude regions (Zou et al., 2017).

Bolch et al. (2019) split High Mountain Asia into 22 subregions based on their topographical and climatological characteristics, of which 13 were situated on the Tibetan Plateau. We selected all 13 subregions as study areas for this work, thus covering most of the Tibetan Plateau (Fig. 1), as well as the Qaidam basin, which was not a subregion in the study by Bolch et al. (2019).

## 3 Data

### 3.1 Planet Basemaps

We used a large amount of optical imagery from high-resolution satellite data, i.e., Planet Basemaps, as training images. Planet Labs Inc. generates the Planet Basemaps product using imagery and data from its fleet with over 200 Earth-imaging satellites (Nass et al., 2019). The three-band (red, green, blue) imagery contain well-processed, scientifically accurate, and analysis-ready mosaics with 4.77 m spatial resolution, visual consistency, and cloud mitigation (Nass et al., 2019). The visual consistency of Planet Basemaps is crucial for developing a comprehensive map of rock glaciers over broad regions. Furthermore, we chose images from a single sensor to ensure consistent quality and timestamp of the source images. To train the deep learning model and infer

new rock glaciers, we mostly utilized the Planet Basemaps mosaics from the third quarter of 2021 (July–September 2021), supplemented with images from the fourth quarter of 2021 (October–December 2021) when needed to mitigate image quality problems in the third-quarter images, such as shadows and image distortion.

### 3.2 Existing rock glacier local inventories for training

To create a set of robust and diverse training data, we compiled existing rock glacier local inventories from multiple regions. Utilizing a multi-source approach helps increase the volume and diversity of the training dataset while mitigating the subjectivity and possible biases introduced by individual inventories. To incorporate more high-quality data, we included rock glaciers not only from the Tibetan Plateau but also from other regions, with a total of six local inventories comprising both intact and relict rock glaciers (Table 1).

Prior to generating the final training dataset, we performed a quality control to account for the various source images and compilation strategies employed among these inventories. As a result, we manually checked and modified rock glacier boundaries by overlaying and visually checking the previously inventoried rock glaciers on our Planet Basemaps images. For example, rock glaciers that were difficult to recognize at places where the image quality was poor or covered by shadows were removed; when we identified missing rock glaciers in previous inventories, these were manually added. Since the front is a critical feature of a rock glacier, we followed the RGIK guidelines to use the extended geomorphological footprints to delineate rock glacier training samples (RGIK, 2023). We finally collected 4085 rock glacier polygons as training samples.

### 3.3 Topo-climatic datasets

To analyze the patterns of rock glacier distribution and the associated environmental factors, we used several topoclimatic datasets, including (1) the 30 m resolution National Aeronautics and Space Administration Digital Elevation Model (NASADEM) (Crippen et al., 2016), (2) the $0.1^\circ \times 0.1^\circ$ monthly mean annual air temperature (MAAT) data from January 1982 to the present derived from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) (McNally and NASA/GSFC/HSL, 2018), (3) the mean annual ground temperature (MAGT) data from 2000 to 2016 at 1 km spatial resolution produced by Obu et al. (2018), and (4) the $0.1^\circ \times 0.1^\circ$ monthly precipitation data from 2001 to 2020 from the Integrated Multi-satellite Retrievals for GPM (IMERG) (Huffman et al., 2023).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Study area (the Tibetan Plateau). The Hindu Kush Himalaya region is excluded from this study. The permafrost extent map is from Obu et al. (2018).</figcaption></figure>

<table><caption>**Table 1.** Information of rock glacier local inventories selected for training the deep learning model.</caption><thead><tr><th>Location</th><th>Number of rock glaciers</th><th>Number of intact rock glaciers</th><th>Number of relict rock glaciers</th><th>Image sources</th><th>Method</th><th>Reference</th></tr></thead><tbody><tr><td>Western Kunlun Shan</td><td>413</td><td>413</td><td>0</td><td>ALOS-1 PALSAR-1, Sentinel-2, Google Earth</td><td>InSAR, deep learning, visual analysis</td><td>Hu et al. (2023)</td></tr><tr><td>Hunza River basin</td><td>616</td><td>450</td><td>166</td><td>Google Earth</td><td>Visual analysis</td><td>Hassan et al. (2021)</td></tr><tr><td>Poiqu River basin</td><td>370</td><td>370</td><td>0</td><td>Pléiades, Google Earth</td><td>Visual analysis</td><td>Bolch et al. (2022)</td></tr><tr><td>Daxue Shan</td><td>295</td><td>Unknown</td><td>Unknown</td><td>Google Earth</td><td>Visual analysis</td><td>Ran and Liu (2018)</td></tr><tr><td>Northern Tien Shan</td><td>551</td><td>Unknown</td><td>Unknown</td><td>ERS-1/2 tandem mission, ALOS-1 PALSAR-1, ALOS-2 PALSAR-2, Sentinel-1, Google Earth, Bing Maps</td><td>InSAR, visual analysis</td><td>Kääb et al. (2021)</td></tr><tr><td>French Alps</td><td>3281</td><td>1498</td><td>1783</td><td>IGN ortho-imagery</td><td>Visual analysis</td><td>Marcer et al. (2017)</td></tr></tbody></table>

## 3.4 Auxiliary data

We also incorporated additional data sources including Google Earth images, ESRI base maps, information on glacier distributions, and information on permafrost distributions. Google Earth images and ESRI base maps were used as supplementary data to aid in the identification and validation of rock glaciers by using high-resolution images (Yu and Gong, 2012). For the clean glacier and debris-covered glacier data, we utilized the widely recognized Randolph Glacier Inventory (RGI v6.0), which provides global coverage of glacier outlines (Pfeffer et al., 2014). The RGI offers a valuable reference for distinguishing rock glaciers from adjacent glaciers. Regarding permafrost extent, we relied on the map for the Northern Hemisphere produced by Obu et al. (2018).

# 4 Methodology

## 4.1 Deep-learning-based rock glacier mapping approach

We propose a systematic deep-learning-based approach for mapping rock glaciers on the Tibetan Plateau. The workflow of the mapping approach is illustrated in Fig. 2. The mapping process comprises two primary stages: (i) deep learning mapping and (ii) manual improvement, which will be elaborated on in the following subsections.

### 4.1.1 Deep learning mapping

DeepLabv3+, introduced by Chen et al. (2018), was selected as the neural network architecture for the deep learning model, with Xception71 serving as its backbone (Chollet, 2017). DeepLabv3+ is specifically designed for semantic segmentation tasks and has proven to excel in mapping
<figure><img src="image_4.png" /><figcaption>Figure 2. Flowchart of the deep-learning-based approach for mapping rock glaciers.</figcaption></figure>

permafrost landforms (Huang et al., 2020; Hu et al., 2023). Xception71 is a convolutional neural network architecture consisting of 71 layers and encompasses approximately 42 million parameters (Chollet, 2017).

Our deep learning model takes a three-channel image with red, green, and blue (RGB) bands as input and outputs a binary image indicating the occurrence of rock glaciers. The topographic information such as slope or elevation was not used as this model only accepts three image bands as input. For the model training, 70% of rock glacier boundaries from the six local inventories were extracted, with the remaining 30% kept for validation. The intersection over union (IoU) was employed as the accuracy metric of validation, which is defined as:

$$IoU(A, B) = \frac{\text{area}(A \cap B)}{\text{area}(A \cup B)}, \qquad (1)$$

where A denotes the mapped polygon and B is the reference polygon. The IoU scores range from 0 to 1, and a higher value indicates a higher accuracy (Huang et al., 2020).

We supplemented our training dataset with negative samples, incorporating non-rock-glacier polygons to address potential misclassifications by the deep learning model, particularly when encountering landforms exhibiting similar characteristics to rock glaciers. These non-rock-glacier polygons

encompass diverse features such as debris-covered glaciers, rock avalanches, and water bodies. To incorporate contextual information from the surrounding area of a rock glacier, we established a buffer area to extract a subset of Planet Basemaps images. A too small buffer area may fail to provide sufficient contextual information, resulting in an increased occurrence of false positives, while an overly large buffer area risks inundating the model with excessive background information, potentially compromising its ability to accurately detect rock glaciers (Huang et al., 2018, 2020). We conducted experiments with buffer area widths ranging from 500 to 2000 m. The results showed no significant increase in the IoU metric once the buffer area exceeded 1500 m. Therefore, we selected 1500 m as the buffer size. The subset of images was then subdivided into patches of 480 × 480 pixels with an overlap of 160 pixels. The binary label patches were created by rasterizing rock glacier polygons (Huang et al., 2020).

Once trained, the deep learning model was validated using images across the Tibetan Plateau. We determined true positives (TPs), false positives (FPs), and false negatives (FNs); then we calculated the precision, recall, and F1 score using
the following equations (Huang et al., 2020):

$$Precision = TP/(TP + FP), \qquad (2)$$

$$Recall = TP/(TP + FN), \qquad (3)$$

$$F_1 = 2 \times Precision \times Recall/(Precision + Recall). \qquad (4)$$

Since the predicted polygons are subject to uncertainties due to varying qualities of imagery, training inventories, and model accuracy, initial results are referred to as “candidate rock glacier polygons”. These polygons did not constitute the final rock glacier inventory but rather served as the preliminary detection of rock glaciers, along with the locations and boundaries, which were then refined as described below.

### 4.1.2 Manual improvement and independent validation

To ensure the accuracy and reliability of the dataset, a manual checking and improving process was carried out on the candidate rock glacier polygons. By utilizing these polygons as a starting point, the subsequent manual compilation efforts were significantly streamlined. The manual improvement process followed the standard guidelines recommended by the IPA Action Group RGIK (RGIK, 2023). According to these guidelines, the mapped rock glaciers were visually checked based on specific geomorphological features, notably the visible accumulation of talus material at the front and the presence of a lateral extension of this talus material along the sides of the rock glacier. Additionally, certain rock glaciers may exhibit noticeable convex-downslope or longitudinal-surface undulations, creating a ridge-and-furrow topography. We considered the extended footprints of rock glaciers while restricting the horizontal distance between the upper front edge and the frontal talus base within 50 m to exclude the possible exaggerated front. Following the global glacier inventory standards and given the resolution limitations of Planet Basemaps (4.77 m), rock glaciers smaller than 10 000 m² (0.01 km²) were excluded from the inventory (RGIK, 2023).

We proposed four “r” operations to manually check the rock glacier candidate polygons:

- remain – no operation if the polygon accurately outlines the rock glacier,

- remove – remove the polygon if it is not a rock glacier,

- refine – modify the polygon if it was correctly identified as a rock glacier but the boundary was not correctly outlined,

- retrieve – add a missing rock glacier and outline its boundary.

The “remove” operation is designed to exclude landforms that have been incorrectly identified as rock glaciers by the deep learning model. These misidentified landforms commonly include debris-covered glaciers and rock avalanches.

Debris-covered glaciers are glaciers that are partially covered by variable layers of debris (from a few centimeters to 2 m) and are characterized by supraglacial features such as thermokarst features, supraglacial lakes, streams, and ice cliffs (Jones et al., 2019a; Racoviteanu et al., 2022; RGIK, 2023). Outlines from the RGI v6.0 inventory were used to visually remove polygons overlapping debris-covered glaciers. Rock avalanches, on the other hand, are composed of fragmented rocks that flow downhill, following large rock slope failures (Hungr et al., 2014). Unlike rock glaciers, rock avalanches typically lack any discernible pattern or order on the surface. The “refine” operation involves the manual editing of the deep-learning-predicted rock glacier outlines to ensure that the polygon boundaries closely matched the observed boundaries of rock glaciers in the images. The “re-trieve” operation serves the purpose of adding missing rock glaciers to the inventory. Some rock glaciers can be overlooked by the deep learning model, due to either their subtle features or their low-quality image data. When missing rock glaciers were identified during the manual inspection of nearby candidate polygons, they were added to the inventory. Furthermore, in high-mountain environments, the convergence of multiple rock glacier units into a complex system is a frequent occurrence (RGIK, 2023). However, the deep learning model often tends to predict this system as a singular rock glacier. To anticipate such issues, we manually separated the system into smaller rock glacier units if their lateral boundaries were clearly observed in Planet Basemaps images.

Our team consisted of seven mappers and two independent reviewers. Each candidate rock glacier polygon was manually examined and refined by visual interpretation of Planet Basemaps images following the four “r” operations by each mapper. In cases where the features of rock glaciers were uncertain and not clearly observable in Planet Basemaps images, high-resolution Google Earth images and ESRI base maps were utilized for more accurate visual inspection and analysis. An extended footprint of each rock glacier was yielded, from which we generated the primary marker, which is a point identifying a unique rock glacier unit or system (RGIK, 2023).

We proceeded with an independent validation process to assess the quality of the revised inventory. Given the difficulty in accurately evaluating the delineated boundaries, our validation focused primarily on verifying the primary markers. To conduct this validation, we randomly selected 2110 samples (approximately 5% of the primary markers). Two independent reviewers examined all the selected samples using Google Earth images. Based on their independent assessments, each reviewer provided one of four decisions: “yes”, indicating that the rock glaciers were correctly identified; “no”, suggesting an incorrect identification; “uncertain”, denoting a lack of certainty in the identification; or “undefined”, used when the examined rock glaciers could not be clearly
observed due to factors such as heavy snow cover, shadows, or the unavailability of high-quality images.

## 4.2 Adding attributes of the final revised rock glaciers

According to the IPA RGIK guidelines, there are three mandatory attributes for a rock glacier unit (RGU): the primary ID, the associated rock glacier system (RGS), and the metadata (RGIK, 2023). In our inventory, the attribute “ID” is equivalent to the primary ID, which is formed by combining “RGU” with the WGS84 coordinates of the rock glacier, expressed in decimal degrees with four digits (RGIK, 2023). We were unable to provide the RGS information in our current inventory due to image resolution limitations and instance segmentation issues. We have included the metadata attribute, which contains information of source data, date of mapping, mapper’s name, reviewer’s name, and additional information, which are separately stored in SOUR_DATA, MAP_DATE, MAPPER, REVIEWER, and ADDI_INF attributes (RGIK, 2023). The ADDI_INF provides information on whether the rock glacier has been recognized as a false identification by the reviewers. Furthermore, we computed the geomorphic and climatic attributes of each inventoried rock glacier to analyze their spatial distribution characteristics and the associated topo-climatic conditions. We derived the rock glacier area based on the polygon extent. The NASADEM was used to calculate the elevation, slope, and aspect of the rock glaciers (Crippen et al., 2016). The climatic information, including MAAT, MAGT, and annual precipitation, of each rock glacier was extracted from the climatic data. We also calculated the annual potential incoming solar radiation (PISR) using the model described by Kumar et al. (1997). Table 2 lists all the attributes of the inventory.

## 4.3 Spatial analysis of rock glaciers

To investigate the spatial distribution characteristics of rock glaciers on the Tibetan Plateau, we conducted statistical analyses of their geomorphic features within a 50 km × 50 km grid cell. In each cell, we counted the number of rock glaciers and calculated the average values for their areas, minimum elevations, and slopes. We also analyzed the distribution patterns of their aspects in different subregions.

# 5 Results

Across the entire study area, the deep learning model predicted a total of 48 767 candidate rock glacier polygons (Fig. S1 in the Supplement). After the manual improvement (see Sect. 4.1.2), we produced an inventory consisting of 44 273 rock glaciers (Fig. 5 and further described in Sect. 5.2 and 5.3). Below, we first present the validation of our results from three perspectives: (i) validation of the deep learning model based on training and validation datasets (Sect. 5.1.1), (ii) validation of the deep-learning-predicted rock glacier

outlines based on manually improved rock glaciers used as our ground truth (Sect. 5.1.1), and (iii) independent validation of inventoried rock glaciers based on visual examination (Sect. 5.1.2).

## 5.1 Performance of deep-learning-based rock glacier mapping approach

### 5.1.1 Deep learning model performance and output

Figure 3a shows the IoU scores achieved by the deep learning model during the training and validating processes. Initially, both the training and validation IoU scores exhibit an upward trend, followed by a gradual stabilization. By the last iteration, the model achieved an IoU score of 0.76 on the training dataset and 0.70 on the validation dataset, indicating that the model learned effectively from the training data and generalized well to the validation data.

To further evaluate the model performance, we applied the well-trained model to predict the rock glacier boundaries on both the training and validation datasets. The deep learning model accurately captured rock glacier characteristics within the training dataset, as evidenced by the close alignment between the predicted boundaries and the training polygons (Fig. 3b). Figure 3c further confirms that the model could generalize well to new datasets, with good agreement between predicted boundaries and validation polygons. However, difficulties in mapping rooting regions led to misalignment in those areas (Brardinoni et al., 2019).

Table 3 presents the calculated recall, precision, and $F_1$ score of the deep learning mapped polygons for each subregion, as well as for the entire study area. Over the entire study area, the $F_1$ score was 0.63, which we consider satisfactory for rock glacier mapping. The highest performance was for the Hengduan Shan ($F_1$ = 0.76), with $F_1$ scores of the Eastern Pamir, Karakoram, Nyainqêntanglha, Western Kunlun Shan, Western Pamir, and Hengduan Shan subregions above 0.6; lower $F_1$ scores for the Altun Shan, Eastern Kunlun Shan, Eastern Tibetan Mountains, and Gangdise Mountains (0.27–0.36); and the lowest score for the Tibetan Interior Mountains subregion (0.16) (Table 3). This disparity arises from the scarcity of rock glaciers in certain subregions, where the deep learning model generated a large number of falsely detected polygons and subsequently produced high-false-positive values. Recall scores are generally higher than the precision scores, indicating that the false positives outweigh the false negatives in the model predictions. This finding suggests that the deep learning model possesses a strong capability for detecting rock glaciers.

However, deep learning alone also generates numerous falsely detected polygons, highlighting the need for manual improvement. For example, Fig. 4 demonstrates the performance of the well-trained deep learning model in detecting and delineating rock glaciers in a new area – the Western Pamir, which was not included in the training process. As
<table><caption>**Table 2.** Attribute data dictionary for the Tibetan Plateau rock glacier inventory shapefile.</caption><thead><tr><th>Attribute name</th><th>Description</th><th>Units</th></tr></thead><tbody><tr><td>ID<sup>1</sup></td><td>Rock glacier ID</td><td></td></tr><tr><td>SOUR_DATA<sup>2</sup></td><td>Source data</td><td></td></tr><tr><td>MAP_DATE<sup>2</sup></td><td>Date of mapping</td><td></td></tr><tr><td>MAPPER<sup>2</sup></td><td>Mapper's name</td><td></td></tr><tr><td>REVIEWER<sup>2</sup></td><td>Reviewer's name</td><td></td></tr><tr><td>ADDI_INF<sup>2</sup></td><td>Additional information</td><td></td></tr><tr><td>LAT</td><td>Latitude</td><td>°</td></tr><tr><td>LON</td><td>Longitude</td><td>°</td></tr><tr><td>SUBREGION</td><td>Subregion of rock glacier</td><td></td></tr><tr><td>AREA</td><td>Rock glacier area</td><td>m<sup>2</sup></td></tr><tr><td>ELE_MEAN<sup>3</sup></td><td>Mean elevation of rock glacier</td><td>m</td></tr><tr><td>ELEhaus</td><td>Median elevation of rock glacier</td><td>m</td></tr><tr><td>ELE_MIN<sup>3</sup></td><td>Minimum elevation of rock glacier</td><td>m</td></tr><tr><td>ELE_MAX<sup>3</sup></td><td>Maximum elevation of rock glacier</td><td>m</td></tr><tr><td>SLO_MEAN<sup>3</sup></td><td>Mean slope of rock glacier</td><td>°</td></tr><tr><td>SLO_MEDIAN<sup>3</sup></td><td>Median slope of rock glacier</td><td>°</td></tr><tr><td>SLO_MIN<sup>3</sup></td><td>Minimum slope of rock glacier</td><td>°</td></tr><tr><td>SLO_MAX<sup>3</sup></td><td>Maximum slope of rock glacier</td><td>°</td></tr><tr><td>ASPECT<sup>3</sup></td><td>Aspect of rock glacier</td><td>°</td></tr><tr><td>MAAT<sup>4</sup></td><td>Mean annual air temperature</td><td>°C</td></tr><tr><td>MAGT<sup>5</sup></td><td>Mean annual ground temperature</td><td>°C</td></tr><tr><td>AP<sup>6</sup></td><td>Annual precipitation</td><td>mm</td></tr><tr><td>PISR<sup>3</sup></td><td>Annual potential incoming solar radiation</td><td>kWh m<sup>-2</sup></td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** (a) IoU scores during the training and validation processes. Examples of the candidate rock glaciers are shown in (b) training and (c) validation regions using the well-trained deep learning model. The IoU scores are labeled on the mapped rock glaciers.</figcaption></figure>

<footnote n="1">¹ ID is identical to the primary ID attribute in the IPA RGIK guidelines.</footnote>

<footnote n="2">² SOUR_DATA, MAP_DATE, MAPPER, REVIEWER, and ADDI_INF contain the information of metadata attribute in the IPA RGIK guidelines.</footnote>

<footnote n="3">³ ELE_MEAN, ELEhaus, ELE_MIN, ELE_MAX, SLO_MEAN, SLO_MEDIAN, SLO_MIN, SLO_MAX, ASPECT, and PASR are attributed based on the 30 m resolution National Aeronautics and Space Administration Digital Elevation Model (NASADEM) (Crippen et al., 2016) (https://search.earthdata.nasa.gov/search, last access: 3 December 2024).</footnote>

<footnote n="4">⁴ MAAT is attributed based on the 0.1° × 0.1° monthly mean annual air temperature (MAAT) data from January 1982 to the present derived from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) (McNally and NASA/GSFC/HSL, 2018) (https://disc.gsfc.nasa.gov/datasets/ FLDAS_NOAH01_C_GL_M_001/summary?keywords=MERRA-2%20and%20CHIRPS, last access: 3 December 2024).</footnote>

<footnote n="5">⁵ MAGT is attributed based on mean annual ground temperature (MAGT) data from 2000 to 2016 at 1 km spatial resolution produced by Obu et al. (2018) (https://apgc.awi.de/dataset/pex, last access: 3 December 2024).</footnote>

<footnote n="6">⁶ AP is attributed based on the 0.1° × 0.1° monthly precipitation data from 2001 to 2020 from the Integrated Multi-satellite Retrievals for GPM (IMERG) (https://doi.org/10.5067/GPM/IMERG/3B-MONTH/07, Huffman et al., 2023).</footnote>
<table><caption>**Table 3.** Performance of deep-learning-mapped polygons in different subregions.</caption><thead><tr><th>Subregion</th><th>Number of<br>deep-learning-<br>mapped polygons</th><th>Number of<br>manually revised<br>rock glaciers</th><th>TP* (km²)</th><th>FP* (km²)</th><th>FN* (km²)</th><th>Precision</th><th>Recall</th><th>F₁ score</th></tr></thead><tbody><tr><td>Altun Shan</td><td>82</td><td>32</td><td>3.55</td><td>9.03</td><td>3.45</td><td>0.28</td><td>0.51</td><td>0.36</td></tr><tr><td>Eastern Kunlun Shan</td><td>517</td><td>180</td><td>22.81</td><td>60.57</td><td>33.46</td><td>0.27</td><td>0.41</td><td>0.33</td></tr><tr><td>Eastern Pamir</td><td>1060</td><td>1330</td><td>230.76</td><td>115.40</td><td>143.12</td><td>0.67</td><td>0.62</td><td>0.64</td></tr><tr><td>Eastern Tibetan Mountains</td><td>2569</td><td>1095</td><td>43.97</td><td>200.09</td><td>32.34</td><td>0.18</td><td>0.58</td><td>0.27</td></tr><tr><td>Gangdise Mountains</td><td>1572</td><td>816</td><td>49.91</td><td>128.50</td><td>66.82</td><td>0.28</td><td>0.43</td><td>0.34</td></tr><tr><td>Karakoram</td><td>2873</td><td>2612</td><td>415.89</td><td>344.57</td><td>133.41</td><td>0.55</td><td>0.76</td><td>0.64</td></tr><tr><td>Nyainqêntanglha</td><td>14161</td><td>16222</td><td>1095.42</td><td>876.49</td><td>465.46</td><td>0.56</td><td>0.70</td><td>0.62</td></tr><tr><td>Qilian Shan</td><td>1367</td><td>1047</td><td>77.21</td><td>129.90</td><td>68.68</td><td>0.37</td><td>0.53</td><td>0.44</td></tr><tr><td>Tibetan Interior Mountains</td><td>1158</td><td>150</td><td>15.71</td><td>130.23</td><td>35.13</td><td>0.11</td><td>0.31</td><td>0.16</td></tr><tr><td>Western Kunlun Shan</td><td>779</td><td>1019</td><td>116.44</td><td>69.40</td><td>87.56</td><td>0.63</td><td>0.57</td><td>0.60</td></tr><tr><td>Western Pamir</td><td>4989</td><td>4957</td><td>685.50</td><td>549.56</td><td>266.32</td><td>0.56</td><td>0.72</td><td>0.63</td></tr><tr><td>Tanggula Shan</td><td>4010</td><td>2402</td><td>166.92</td><td>288.34</td><td>61.34</td><td>0.37</td><td>0.73</td><td>0.49</td></tr><tr><td>Hengduan Shan</td><td>13387</td><td>12411</td><td>1478.55</td><td>678.96</td><td>268.31</td><td>0.69</td><td>0.85</td><td>0.76</td></tr><tr><td>Qaidam</td><td>243</td><td>0</td><td>0</td><td>15.95</td><td>0</td><td>0</td><td>N/A</td><td>N/A</td></tr></tbody><tfoot><tr><td>Entire study area</td><td>48767</td><td>44273</td><td>4403.43</td><td>3596.49</td><td>1664.61</td><td>0.55</td><td>0.73</td><td>0.63</td></tr></tfoot></table>

shown in Fig. 4a-c, there is good agreement between the deep learning output and the manually revised boundaries for a significant proportion of the rock glaciers in this area. However, Fig. 4 also illustrates some uncertainties associated with inaccurate boundary delineation, false detections, and missing identifications. For instance, as shown in Fig. 4d, a debris-covered glacier was falsely identified as a rock glacier, while Fig. 4e highlights several missing rock glaciers, possibly due to their poorly developed geomorphological features.

### 5.1.2 Independent validation of the inventoried rock glaciers

Results of the independent review based on the 2110 rock glacier primary markers are presented in Table 4 and show that approximately 87% of the primary markers were assigned as correctly identifying rock glaciers by both reviewers. This indicates that most of the sampled features met the criteria and characteristics of rock glaciers. Additionally, the evaluation process identified that only approximately 1% and 6% of the primary markers were assigned as false identifications by the first and second reviewers, respectively. This signifies that the occurrence of misclassifications or false positives within the inventory is relatively low (below 10%). The discrepancy in the “no” decision numbers between the two reviewers can be attributed to the differences in the operators’ judgments (Brardinoni et al., 2019).

## 5.2 Rock glacier inventory on the Tibetan Plateau: TPRoGI (v1.0)

After manual improvement, our plateau-wide inventory encompasses 44273 rock glaciers, including both intact and relict types (Fig. 5). The inventoried rock glaciers cover a total area of approximately 6000 km² (6068043348 m²). The mean area is 0.14 km². The largest rock glacier occupies 4.6 km², whereas most of them (90.6%) are smaller than 0.3 km² (Fig. 6a). In terms of elevation, most rock glaciers (95.0%) exhibit minimum elevations between 4000 and 5500 m above sea level (m a.s.l.), with an average value of 4729 m a.s.l. (Fig. 6b). The highest rock glacier is situated at an elevation of 5839 m a.s.l. in the Tibetan Interior Mountains, whereas the lowest lies at 2717 m a.s.l. in Western Pamir. Rock glaciers develop on slopes with varying gradients, and approximately 90% of them occur on slopes between 10 and 25° with an average slope angle of 17.7° (Fig. 6c). Also, the compiled rock glaciers are distributed across various slope orientations with preferences at the north- and west-facing slopes (Fig. 6d).

Rock glaciers predominantly occur in cold environments with temperatures at or slightly below freezing. A significant proportion of rock glaciers (66.3%) thrive in areas where the MAAT ranges between -5 and 0°C (Fig. 6e). Furthermore, 71.7% of the rock glaciers exhibit MAGT between -5 and 0°C (Fig. 6f). On average, the MAAT and MAGT for these rock glaciers are -2.7 and -1.6°C, respectively. Approximately 82% of the rock glaciers are situated in regions with annual precipitation ranging from 300 to 1000 mm, with an average of 597 mm (Fig. 6g). About 85% of the rock glaciers receive incoming solar radiation

<footnote>* TP (true positive), FP (false positive), and FN (false negative) are expressed as the total areas.</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 4.** (a) An example area in Western Pamir showing the deep learning outputs (in yellow) and manually revised rock glacier boundaries (in green). Clean and debris-covered glacier extents (light blue) are from the Randolph Glacier Inventory (RGI v.6) (Pfeffer et al., 2014); (b, c) enlarged views of the areas showing good agreement between deep learning outputs and revised boundaries; (d) enlarged view showing a false detection example in the center; (e) enlarged view showing multiple missing identifications.</figcaption></figure>

<table><caption>**Table 4.** Independent validation results of sampled Tibetan Plateau rock glacier inventory (n = 2110 samples).</caption><thead><tr><td>Reviewer</td><td>Number of “yes”</td><td>Number of “no”</td><td>Number of “uncertain”</td><td>Number of “undefined”</td></tr></thead><tbody><tr><td>Reviewer 1</td><td>1836</td><td>17</td><td>42</td><td>215</td></tr><tr><td>Reviewer 2</td><td>1844</td><td>127</td><td>44</td><td>95</td></tr></tbody></table>

(PISR) between 2500 and 3500 kWh m⁻² annually, with a mean value of 2930 kWh m⁻² (Fig. 6h).

## 5.3 Spatial distribution characteristics of rock glaciers

Figure 7 presents the spatial distribution and geomorphic characteristics of rock glaciers on the Tibetan Plateau within

50 km grid cells. Rock glaciers are widespread in the north-western and southeastern plateau and densely distributed in the Western Pamir and Nyainqêntanglha, while they are scarce in the inner plateau (Fig. 7a). No rock glaciers were found in the Qaidam region, presumably due to the absence of permafrost and the occurrence of few mountains there. Rock glaciers in the western plateau have larger ar-
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Rock glacier inventory on the Tibetan Plateau (TPRoGI). The permafrost in the Hengduan Shan is overlapped by rock glaciers and thus is not visible on the map. The permafrost extent map is from Obu et al. (2018).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Statistical summaries of the geomorphic and current climatic features of rock glaciers in the study region. (a) The areal histogram of all rock glaciers on the Tibetan Plateau. The inset shows the areas smaller than 0.5 km². Panels (b)–(h) show histograms of the minimum elevations, slopes, aspects of the rock glaciers (with the radial axis representing the counts), mean annual air temperature (MAAT), mean annual ground temperature (MAGT), annual precipitation, and annual potential incoming solar radiation (PISR), respectively.</figcaption></figure>
eas (mean = 0.21 km²) than in the eastern plateau (mean = 0.11 km²), as evident in Fig. 7b. Notably, a decreasing gradient is observed in minimum elevations of rock glaciers, with higher elevations in the Gangdise Mountains and lower elevations towards the east and west directions (Fig. 7c). The average slopes of rock glaciers are larger in northwestern Karakoram and southeastern plateau, suggesting a tendency for rock glaciers to develop on steeper slopes in these areas (Fig. 7d).

Rock glacier aspects across different subregions are depicted in Fig. 8, characterized by a discernible west-east gradient and similarities between neighboring subregions. Specifically, the ones found in the western plateau (Western Kunlun Shan, Karakoram, Eastern Pamir, Western Pamir) display no distinct preference towards any specific orientation, whereas those situated in the central part of the plateau (Altun Shan, Eastern Kunlun Shan, Tibetan Interior Mountains, Gangdise Mountains) primarily face north. Conversely, rock glaciers in the eastern plateau (Qilian Shan, Eastern Tibetan Mountains, Tanggula Shan, Hengduan Shan, Nyainqêntanglha) exhibit a prevalent preference for north and west orientations.

# 6 Discussion

## 6.1 Limitations of the deep-learning-based mapping approach

### 6.1.1 Limitations of Planet Basemaps imagery

The quality of the source images plays a crucial role in the uncertainty of the predicted results as the deep learning model accuracy heavily relies on high-quality input images. However, rock glaciers are frequently found in regions characterized by poor image quality due to factors associated with cloud cover, shadows, and distortions, which are common in mountainous areas. These challenges have a substantial impact on the accuracy of predictions. Consequently, when the deep learning model is input with images suffering severe quality issues, it may fail to identify rock glaciers within that region (Fig. S5 in the Supplement).

### 6.1.2 Limitations of the deep learning model

The mapped results generated by the deep learning model still have significant uncertainties associated with inaccurately predicted boundaries, false detections, and missing identifications (Fig. 4). Despite utilizing the powerful neural network DeepLabv3+ as the model structure, the training and validation IoU scores fall below 0.8 (Fig. 3). When applied to the entire study area, the uncertainty increases further, with a precision of 0.55, a recall of 0.73, and a $F_1$ score of 0.63 (Table 3). These results are comparable to the results by Robson et al. (2020), which obtained a precision of 63.9% to 68.9% and a recall of 75% to 75.4%. Both results highlight

the challenges of using deep learning to fully automatically map rock glaciers in high-mountain environments.

A key limitation of the current deep learning model is the restricted number of input bands. Our model only utilizes RGB bands, while inherently excluding crucial topographic information such as slope and elevation. As rock glacier occurrence is closely related to topography and underlying geology, the absence of morphometric inputs like terrain roughness and slope, as well as lithological data, may hinder the model performance.

Furthermore, the learning performance of the model can be hindered by limited and biased training samples. Our training samples were derived from six local inventories, encompassing 4085 rock glacier polygons. Due to the limited quantity of our training dataset, the model may struggle to fully capture the complexity and diversity of the training samples. Consequently, its generalization ability and accuracy may be compromised when presented with unfamiliar images (Rice et al., 2020). Additionally, the six local inventories were compiled by different operators from various institutes. The divergent knowledge and expertise among these operators can introduce inconsistencies in judgments, resulting in subjectivity and bias within the training dataset. As a result, inconsistent and biased training samples can potentially confuse the model, thereby impairing its ability to accurately identify rock glaciers (Ren et al., 2018).

Additionally, it is important to note that the deep learning model can only map the areas of rock glaciers and is not capable of performing instance segmentation, which would accurately segment individual rock glacier units (Erharter et al., 2022). Consequently, the model tends to predict the entire rock glacier system, composed of several adjacent rock glacier units, as a single entity.

### 6.1.3 Limitations of manual improvement

The manual examination and refinement were assigned by multiple individuals with varying levels of experience, which inevitably introduced subjectivity, human errors, and potential inconsistencies (Brardinoni et al., 2019). Moreover, accurately depicting the boundaries of rock glaciers via manual delineation can be challenging due to the 4.77 m resolution of the interpreted images; thus, the mapped rock glaciers inherently contain uncertainties (Jones et al., 2018).

Furthermore, delineating the upper and lateral boundaries within rock glacier systems presents even greater uncertainties (Brardinoni et al., 2019). In comparison to the lower boundary in the front and lateral margin regions, the upper boundary in the rooting zone and the lateral boundary between rock glaciers within a system often lack pronounced geomorphological features and thereby require more precise interpretation of surface texture and color variations. As a result, the delineation of upper and lateral boundaries is inherently ambiguous and subjective (Schmid et al., 2015; Jones et al., 2018; Erharter et al., 2022). Due to the difficulty in
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Rock glacier (a) density, (b) area, (c) minimum elevation and (d) slope averaged over grid cells of 50 km × 50 km.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 8.** Rock glacier aspects in different subregions of the study area.</figcaption></figure>

delineating lateral boundaries and the limitations imposed by image resolution, the separation of rock glacier systems is uncertain. Therefore, some rock glacier systems, particularly the smaller ones lacking pronounced features of lateral boundaries, may not be effectively separated (Fig. S4 in the Supplement).

Additionally, the “retrieve” operation focused on areas where missing rock glaciers were observed near the polygons identified by the deep learning model. Consequently, some rock glaciers may have been missed without conducting an exhaustive examination of the entire study region.

## 6.2 Comparison with existing local inventories

We compared the number of inventoried rock glaciers in our study with existing local inventories on the plateau, including Daxue Shan (Ran and Liu, 2018; Cai et al., 2021), Nyainqên-tanglha (Reinosch et al., 2021; Zhang et al., 2023; Li et al., 2024), Hunza Basin (Hassan et al., 2021), Gangdise Mountains (Zhang et al., 2022), Western Kunlun Shan (Hu et al., 2023), and Qilian Shan (Hu et al., 2024) as shown in Table 5. The number of inventoried rock glaciers in our study is generally comparable to those in Daxue Shan and Hunza Basin. However, our inventory has more rock glaciers than the inventories in Gangdise Mountains and Western Kunlun Shan
and fewer rock glaciers than the inventories in Nyainqêntan-
glha and Qilian Shan.

These discrepancies can be explained by inherent sources
of error within each dataset. As highlighted in the RGIK
guidelines (RGIK, 2023), operator judgment in compiling
rock glacier inventories can vary over time, leading to dis-
crepancies between inventories created at different time peri-
ods. Even within the same time frame, differences in opera-
tor experience can result in significant variations in judgment
(Brardinoni et al., 2019). For example, the delineation of the
upper boundary of rock glaciers in rooting regions is chal-
lenging and can vary among different operators (Brardinoni
et al., 2019). In the Hunza Basin, our delineated rock glaciers
had lower upper boundaries compared to the results of Has-
san et al. (2021) (Fig. S2 in the Supplement). Additionally,
small rock glaciers can be difficult to recognize due to the
lack of distinct characteristics. In the Nyainqêntanglha re-
gion, some small landforms were included as rock glaciers in
the inventories by Reinosch et al. (2021) and Li et al. (2024)
but were excluded from our inventory (Fig. S3 in the Supple-
ment). Moreover, it is common in mountainous environments
for several rock glacier units to merge into a complex system
(RGIK, 2023). Some operators may delineate this system as
a single polygon, while others may separate it into smaller
polygons. This can be observed in the case of Daxue Shan,
where some systems were delineated as single polygons in
our inventory but were separated into smaller polygons in
the inventories of Ran and Liu (2018) and Cai et al. (2021)
(Fig. S4).

Another significant factor contributing to discrepancies in
inventories is the use of different image sources. Images with
varying types, resolutions, and qualities can greatly influence
the inventory results. The use of InSAR images, for exam-
ple, is beneficial for detecting actively moving rock glaciers
but may have poor performance in identifying slowly mov-
ing or relict rock glaciers (Liu et al., 2013; Hu et al., 2023).
Moreover, images with low resolution used in some of the
previous inventories may not clearly reveal the morpholog-
ical characteristics of rock glaciers, increasing the probabil-
ity of missing identifications. In the Western Kunlun Shan
region, our inventory compiled more rock glaciers by using
Planet Basemaps images (4.77 m resolution) compared to Hu
et al. (2023), whose inventory was based on Sentinel-2 im-
ages (10 m resolution). Additionally, images with quality is-
sues caused by clouds, snow, shadows, and image distortion
can lead to missed identifications of rock glaciers. In some
areas of Nyainqêntanglha, for instance, some rock glaciers
were obscured by clouds in Planet Basemaps images and
were missed in our inventory, but they were visible in Google
Earth images and had been included in the inventories by
Reinosch et al. (2021) and Li et al. (2024) (Fig. S5). Since
the discrepancies between inventories can arise from vari-
ous sources, conducting further quantitative comparisons on
the accuracies of rock glacier locations and boundaries poses
challenges.

6.3 Significance of the inventory and future work

To our knowledge, the creation of the new inventory on the
Tibetan Plateau represents the most extensive collection of
rock glaciers published worldwide. This large dataset of-
fers exciting prospects for advancing various research areas
related to rock glaciers, including permafrost distribution,
mountain hydrology, climate impacts on the permafrost en-
vironment, and geohazards as introduced in Sect. 1.

First, our new inventory enables more accurate assess-
ments of permafrost distribution, allowing researchers to re-
fine existing permafrost maps and enhance our understanding
of permafrost characteristics on the Tibetan Plateau (Schmid
et al., 2015; Hassan et al., 2021; Zou et al., 2017; Li et al.,
2024). We underline that the lack of comprehensive rock
glacier information on the plateau had previously limited per-
mafrost assessment studies in this region. Cao et al. (2021)
found that a model driven by rock glacier observations led
to an overestimation of permafrost extent by about 8.4%–
13.1% on the Tibetan Plateau compared to a model using
in situ measurements. Nevertheless, they used datasets from
the Himalayan range as an alternative due to the limited
availability of rock glacier observations on the plateau.

With respect to hydrology, Jones et al. (2021a) had esti-
mated the global water contribution from rock glaciers and
highlighted the lack of rock glacier data in certain regions, in-
cluding the Tibetan Plateau. Our inventory fills the data gap
in this critical region of High Mountain Asia, providing an
opportunity to investigate the potential water storage avail-
able within rock glaciers (Corte, 1976; Azócar and Brenning,
2010; Jones et al., 2019b; Schaffer et al., 2019; Wagner et al.,
2020, 2021) and the contribution of rock glacier meltwater to
streamflow (Geiger et al., 2014; Wagner et al., 2016).

Moreover, our inventory serves as a guide for establishing
rock glacier monitoring sites on the plateau, contributing to
the study of the long-term evolution of rock glaciers and the
impacts of climate change on mountain permafrost in this
region. Systematic monitoring of rock glacier velocities has
been established in the European Alps (e.g., Delaloye et al.,
2010; Marcer et al., 2021), Northern Tien Shan (Kääb et al.,
2021), and the Andes (Vivero et al., 2021). Currently, no such
monitoring sites exist on the Tibetan Plateau due to the lack
of information on rock glacier distribution.

Lastly, the new inventory developed in this study will con-
tribute to the evaluation of rock glacier hazards and risks,
providing important information for geohazard management
and enabling informed decision-making regarding infrastruc-
ture planning on the Tibetan Plateau (Hassan et al., 2021;
Janke and Bolch, 2021).

This benchmark dataset will be maintained and updated
in the future. We will leverage multi-source datasets, in-
cluding InSAR data, elevation change maps (from high-
resolution DEM differencing), and high-resolution optical
images (from Google Earth, ESRI base maps, and Bing
maps), to validate and refine our inventory. The InSAR
<table><caption>**Table 5.** Comparisons of the numbers of inventoried rock glaciers with existing local inventories.</caption><thead><tr><th>Location</th><th>Reference of existing local inventory</th><th>Number of inventoried rock glaciers in previous inventory</th><th>Number of inventoried rock glaciers in this study</th></tr></thead><tbody><tr><td>Daxue Shan</td><td>Ran and Liu (2018)</td><td>295</td><td>256</td></tr><tr><td>Daxue Shan</td><td>Cai et al. (2021)</td><td>344</td><td>256</td></tr><tr><td>Western Nyainqêntanglha Range</td><td>Reinosch et al. (2021)</td><td>1433</td><td>798</td></tr><tr><td>Hunza Basin</td><td>Hassan et al. (2021)</td><td>616</td><td>647</td></tr><tr><td>Gangdise Mountains</td><td>Zhang et al. (2022)</td><td>132</td><td>816</td></tr><tr><td>Western Kunlun Shan</td><td>Hu et al. (2023)</td><td>413</td><td>2145</td></tr><tr><td>Nyainqêntanglha</td><td>Zhang et al. (2023)</td><td>20531</td><td>16222</td></tr><tr><td>Guokalariju</td><td>Li et al. (2024)</td><td>5057</td><td>4000</td></tr><tr><td>Qilian Shan</td><td>Hu et al. (2024)</td><td>1530</td><td>1047</td></tr></tbody></table>

data will be used to attribute kinematic information (RGIK, 2023).

We will extend our inventory in the future by including the Himalayas, the Hindu Kush, and the Tien Shan regions to compile a comprehensive inventory for High Mountain Asia (Fig. S6 in the Supplement). Work is currently ongoing to evaluate the rock glacier kinematics on the Tibetan Plateau based on the developed inventory (Sun et al., 2024b), as well as to validate the deep learning rock glacier output for the Hindu Kush Himalaya regions (Racoviteanu et al., 2024).

## 7 Data availability

The rock glacier inventory for the Tibetan Plateau can be accessed at https://doi.org/10.5281/zenodo.10732042 (Sun et al., 2024a).

## 8 Conclusions

In this study, we proposed a deep-learning-based approach for mapping rock glaciers and created the first plateau-wide inventory, i.e., TPRoGI (v1.0), encompassing 44 273 rock glaciers. This inventory fills the gap in the rock glacier data on the Tibetan Plateau and provides a baseline dataset for monitoring mountain permafrost in this region. Findings from the current study are summarized as follows: (1) the deep learning model demonstrates a promising capability in detecting and outlining rock glaciers and can serve as a valuable tool for inventorying rock glaciers across large regions; (2) rock glaciers are widespread in the northwestern and southeastern plateau and densely distributed in the Western Pamir and Nyainqêntanglha, while they are scarce in the inner plateau; (3) the majority of rock glaciers are situated at elevations from 4000 to 5500 m a.s.l. and on slopes between 10 and 25° with north and west preferences; (4) rock glaciers show a northwest preference in the eastern plateau, a north-only orientation in the central plateau, and no specific preference in the western plateau; (5) rock glaciers on the Tibetan Plateau cover a total area of 6000 km² with a mean area of

0.14 km², with rock glaciers in the western plateau exhibiting larger areas compared to those in other areas. However, there remain uncertainties in the current inventory due to limitations inherent in the imagery, the deep learning model, and the manual revisions. The inventory will continue to be maintained and updated in the future. We expect that the benchmark dataset produced in this study will facilitate investigations into many scientific questions related to rock glaciers and mountain permafrost on the Tibetan Plateau.

**Supplement.** The supplement related to this article is available online at: https://doi.org/10.5194/essd-16-5703-2024-supplement.

**Author contributions.** YaH, LL, and SH designed the study with funding from the Hong Kong Research Grants Council and the CUHK–Exeter Joint Centre for Environmental Sustainability and Resilience. ZS and YaH performed the training and prediction of deep learning model. ZS, YaH, AR, JC, XG, YuH, and HY compiled the inventory. LL and XW reviewed and validated the inventory. ZS wrote the manuscript with input from YaH, AR, LL, and SH.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** We would like to thank the Office National des Forêts (Departement Service Restauration des Terrains en Montagne) for providing the rock glacier inventory in the French Alps, Lingcao Huang for providing the deep learning codes, and Ho Ming Tsang for valuable assistance in the calculation of aspect angles.
**Financial support.** This work has been supported by the Hong Kong Research Grants Council, University Grants Committee (grant nos. CUHK14302421, HKPFS PF20-42392), the The Chinese University of Hong Kong–Exeter Joint Centre for Environmental Sustainability and Resilience (ENSURE, grant no. 4930821), the CUHK Direct Grant for Research (grant no. 4053510 and 4053592), the National Natural Science Foundation of China (grant nos. 42371458, 42071410), and the “PHC PROCORE” program (project number: F-CUHK404/22), funded by the French Ministry for Europe and Foreign Affairs, the French Ministry for Higher Education and Research, and the Hong Kong Research Grants Council. Adina Racoviteanu’s work is supported by the French National Research Institute for Sustainable Development (IRD).

**Review statement.** This paper was edited by James Thornton and reviewed by Flavius Sirbu and two anonymous referees.

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