the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Mar 2024
Interactive Snow Avalanche Segmentation from Webcam Imagery: results, potential and limitations
Abstract. For many safety-related applications such as hazard mapping or road management, well documented avalanche events are crucial. Nowadays, despite research into different directions, the available data is mostly restricted to isolated locations where it is collected by observers in the field. Webcams are getting more frequent in the Alps and beyond, capturing numerous avalanche prone slopes several times a day. To complement the knowledge about avalanche occurrences, we propose to make use of this webcam imagery for avalanche mapping. For humans, avalanches are relatively easy to identify, but the manual mapping of their outlines is time intensive. Therefore, we propose to support the mapping of avalanches in images with a learned segmentation model. In interactive avalanche segmentation (IAS), a user collaborates with a deep learning model to segment the avalanche outlines, taking advantage of human expert knowledge while keeping the effort low thanks to the model's ability to delineate avalanches. The human corrections to the prediction in the form of positive clicks on the avalanche or negative clicks on the background result in avalanche outlines of good quality with little effort. Relying on IAS, we extract avalanches from the images in a flexible and efficient manner, resulting in a 90 % time saving compared to conventional manual mapping. If mounted in a stable position, the camera can be georeferenced with a mono-photogrammetry tool, allowing for exact geolocation of the avalanche outlines and subsequent use in geographical information systems (GIS). In this way all avalanches mapped in an image can be imported into a designated database, making them available for the relevant safety-related applications. For imagery, we rely on current and archive data from webcams that cover the Dischma valley near Davos, Switzerland and capture an image every 30 minutes during daytime since the winter 2019. Our model and the associated mapping pipeline represent an important step forward towards continuous and precise avalanche documentation, complementing existing databases and thereby providing a better base for safety-critical decisions and planning in avalanche-prone mountain regions.
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Status: open (until 31 May 2024)
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RC1: 'Comment on egusphere-2024-498', Anonymous Referee #1, 17 Apr 2024
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General comments:
The manuscript presents a novel dataset using state-of-the-art methodology that will benefit the avalanche research community. I think the novelty of the research is significant for publication, but the clarity of the writing needs to be improve. I suggest a few comments, especially in the methods section, that will enhance the clarity of the manuscript before publication. In addition, there is a lot of syntax errors (missing comas) that affect the general comprehension and the quality of the manuscript. I give more details in the specific and technical comments about these syntax errors.
Specific comments:
The introduction is well structured and the problematic well described.
Most of the method section is well written but some refinements are necessary to improve clarity in the section. My first specific comment is that many processing, model tuning, validation and testing was done on multiple datasets. I suggest the authors to dedicate a general paragraph at the beginning of the method section, that defines more the overall analysis of the paper. I spent quite a time figure out which analysis will be done on which dataset.
I understand that you split your dataset into a train, validate and test dataset. However, the difference between validation and test was not obvious at the beginning since the terms used are both similar in statistical validation context. It wasn’t explain until I read the last section of the method. I suggest you change the word “validate” for maybe “tuning” or something else, because this dataset was only been used to tune the hyperparameter (if I understand correctly).
The section model architecture is very technical, which is not bad, but it might be difficult to understand for the avalanche research community. This community is gonna be very interested in the dataset, but might not be very specialized in that field of IOS (like myself). Maybe you can add a few sentences to highlight the key element of the algorithm HRNet+OCR in a broader sense, before entering into the technical details describing how you adapt the original algorithm to avalanches.
The present tense was generally used to write the method and the result section, where usually the past tense is used in these sections. Please change to the past tense as the calculations and analysis were made in the past. There is also a few present tense that should be in the past tense, as you refers to your results.
There is a lot of missing comas within the manuscript that affect the comprehension of the sentences. These missing comas are always in the beginning of the sentence like for example :”In our user study the participants...” where there should be a coma between study and the participants. There is also cases where the coma is not missing. Please correct these missing comas to enhance the comprehension of the text. See technical comments for more.
Technical corrections:
Intro:
Line 28: Missing coma after “Depending on the source, “
Line 34 : Maybe change heightens to increases.
Line 39 : In opposition instead of opposed.
Line 50 : This sentence is unclear with a few missing comas. Maybe change it for “ However, where satellite data is available, areas affected by avalanches....”
Line 52: the link to the second part of this sentence is not clear, please rephrase or add a few words after the coma to make the link clearer between the canonical problem and instance segmentation for clarity.
Line 67 : Gaussians?
Line 69 : missing coma before “but”.
Line 73 : missing coma between “georeferencing” and “the”.
Line 75 : remove the word “make”.
Line 78 : I don't understand the words “real world application” in your objective. While I think the user study is significant and interesting, I think real world application doesn't apply to the analysis made.
Data:
Line 85 : Suggestion : “Our webcams network covered the whole Dischma valley...”
Line 86 : I think the unit needs to be separate from the value in EGU pub like “ 13 km”.
Line 98 : The sentence “With steep mountains on both sides of the valley over
80% of the entire area are potential avalanche terrain” needs to be rephrase for clarity.
Line 99 : Missing coma between “settlements” and “avalanches”
Figure 1: It is unclear to me which field view correspond to which cameras. Please consider using polyline (transparent in the middle) instead of polygon. Add arrows to clearly show which direction the cameras are looking. I think there is too many details with the swisstopo background that are not needed. I suggest removing some of them could enhance the clarity of the map.
Line 104 : Missing coma after “training”.
Line 105 : Missing coma after “our user stud”.
Line 109 : Missing coma after “validation” and before “while, and after “this” and “we”.
Line 113 : Missing coma “For our user study, we relied”.
Line 123 : Missing coma “validation, which” and “dataset, we”.
Line 125 : Missing coma “comparison, their”.
Methods:
Line 132 : Is it 5 pixels?
Line 133 : Missing coma “segmentation, the”.
Line 137 : Missing coma “implementation, it”.
Line 138 : Does that means that Images are randomly selected during the training for the a user to manually click on the images. Maybe add a sentence to state when the fine tuning is made with the user input (Figure 4). Is it on every images or randomly selected?
Line 142 : Missing coma “mode, the”
Line 157 : Missing coma “the masks, we report”.
Line 159 : Unclear formulation “since the we aim for a high”.
Line 162 : Missing coma “object-level, we compare”.
Line 163 : Please change the letter t for the threshold, as t is already use to explain time step in Figure 4.
Line 164 : Missing coma “Like Fox et al. (2023), we first”.
Line 167 : Missing coma “the matches, we compute”.
Line 173 : Missing coma “our webcam imagery, we evaluate”
Line 180 : is it hyperparameters?
Line 183 : Missing coma “In addition, we compare”.
Line 190 : I suggest to put “we carried out a small user study” at the beginning of the sentence.
Line 191 : Missing coma “user study, we used”.
Line 192 : Missing coma “per click, as well”, since its a enumeration.
Line 194 : Missing coma “in UserPic, the participant”.
Line 197 : Missing coma “user study, we report”.
Line 198 : Missing coma “the NoC 20 @85, as well”, since its a enumeration.
Line 201 : Missing coma “significant, we used”.
Results:
Line 204 : The beginning of this sentence is unclear “Evaluating on the SLF test the AvaWeb”.
Line 208 : Missing coma “baseline, all models”.
Line 209 : Missing coma “Overall, the”.
Line 212 : Missing coma “analyses, we are”.
Figure 6 : Please take the same font as in the text for the figure.
Line 217 : Missing coma “For all models, the images”.
Figure 7 : Please specify what is GT? Is it ground truth?
Line 220 : Missing coma “GroundPic, the AvaWeb”, and “about 10%, while it”.
Line 224 : Missing coma “those avalanches, the IoU”.
Line 225 : Missing coma “avalanches, the AvaWeb”.
Line 226 : Unclear sentence “while this for the AvaPic and AvaMix this is the case for less than 1% of all avalanches”.
Line 227 : Missing coma “same images, which depict”.
Line 230 : Missing coma “bounding boxes, the AvaWeb”.
Line 232 : Missing coma “AvaMix, the F1”.
Line 235 : Missing coma “User Study, we loaded”.
Figure 8-9-10 : The font style is not consistent in this figure.
Line 241 : Missing coma “On average, participants”.
Line 248 : Missing coma”User study, we observed”.
Line 250-251 : Please rephrase this sentence to make it more clear, or maybe make two sentences.
Line 254 : Please rephrase this sentence to make it more clear , especially the end:”While they are not for IoU@1 and IoU@2 (t-test: p-value:
> 0.05), for IoU@3 (p-value= 0.045), IoU@4 (p-value= 0.034) and IoU@5 (p-value= 0.035) they are.”
line 257 : Replase “eachother” by “each other”.
Discussion:
Line 263 : The syntax of this sentence is problematic, maybe remove “outlines” to make it more clear or rephrase it.
Line 265 : Missing coma “(GroundPic), but fails”.
Line 271 : Missing coma “SLF dataset, help”, and also maybe before “following”.
Line 276 : Missing coma “the avalanche, resulting in”.
Line 277 : Missing coma “But overall, the AvaWeb”.
Line 279 : Missing coma “approximately 20% lower IoU”.
Line 281 : Missing coma “imagery, which the model”.
Line 282 : Missing coma “this paper, but for”. Maybe try “this paper but, future work should consider experimenting...”
Line 284 : Missing coma “automated method, Fox et al. (2023)”, and “overlap, which is”.
Line 286 : Add the word “that” in “We capture the area that the avalanche covered...”
Line 288 : Missing coma “user study, the participants. And also the term User study is sometimes written with a capital U and sometimes not, this needs to be consistent.
Line 288 : In this sentence “the best performance are as good as the simulation”, I think the past tense “were” is more appropriate.
Line 294 : Missing coma “manual mapping, using IAS”.
Line 295 : Missing coma “average size 1.75), that take less”
Line 296 : Missing coma “new avalanches, the user”.
Line 297 : Missing coma “Hafner et al. (2023), the mean”.
Line 298 : I think the past tense (were) is more appropriate in “ are within 5% of each other and all have an IoU”
Figure 14 : Missing North arrow in the map.
Line 305 : Missing coma “the winter, leading to more”
Line 206 : The coma should be after “however”, not “requires”.
Line 309 : Missing coma “Without that, the application”.
Line 310 : Missing coma “warning service, while all other”.
Line 316 : Missing coma “mapping avalanches IAS saves”.
Line 317 : This sentence is unclear “since the avalanches were time was recorded were rather small”.
Line 317 : Why is this sentence being one paragraph?
Conclusion:
Line 321 : Past tense “were” in “the predictions are simulated,”.
Line 322 : Missing coma “With IAS, a human user”.
Line 324 : Missing coma “60 minutes, increases the likelihood”.
Line 331 : Missing coma “is stable, the georeferencing”.
Line 331 : The last part of the sentence in unclear, please rephrase “like done before for webcam-based snow cover monitoring (Portenier et al., 2020)”.
Line 332 : Missing coma “In the future, existing approaches”.
Line 338 : Missing coma “more reliable, compared to the”.
Line 343 : I would remove “as is “ in “The model as is may also be used to”.
Line 343 : Replace “These” by “this”.
Line 345 : Maybe add “avalanche annotations” to “thereby getting more accurate and reliable avalanche annotations in the future.”.
Line 345: Missing coma “Overall, this”.
Citation: https://doi.org/10.5194/egusphere-2024-498-RC1
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