UBO Team @ CLEF SimpleText 2023 Track For Task 2
and 3 - Using IA Models To simplify Scientific Texts
Quentin Dubreuil1
1

Université de Bretagne Occidentale, Brest, France

Abstract

Due to the complexity of their language and the requirement for background knowledge, scientific
literature is typically avoided by the general public. Instead, people often rely on superficial andsecondary sources found on the internet and social media, which are frequently motivated by commercial
or political interests rather than providing accurate information. Therefore, we will try to simplify
scientific texts by participating in the SimpleText track. We will begin by introducing the different tasks,
then we will move on to an overview of the different models used, and then we will finish by the resultsof the different tasks, and the conclusion.

Keywords

CLEF, SimpleText, SimpleT5, BLOOM, pke

1. Introduction
Scientific research publications pose challenges for the general public and even for scientists
outside the specific field. The CLEF 2023[1] SimpleText[2, 3] track is unique in its emphasis on
simplifying scientific texts, with a particular focus on making them accessible to the generalpublic. This track combines information retrieval and natural language processing to address
the needs of both readability and comprehension.
The topics of text complexity, reading levels, and text simplification have been extensively
explored in fields such as linguistics, education science, and natural language processing.
Simplified texts offer greater accessibility to non-native speakers, young readers, individuals
with reading disabilities, those in need of reading assistance (e.g., congenitally deaf people), or
individuals with a lower level of education. However, improving text comprehensibility remainsa challenge, as determining the desired output of simplification is inherently complex. The
SimpleText track is divided in 3 tasks• Task 1 : What is in (or out)? Selecting passages to include in a simplified summary• Task 2 : What is unclear? Difficult concept identification and explanation (definitions,abbreviation deciphering, context, applications,..)• Task 3 : Rewrite this! Given a query, simplify passages from scientific abstracts
We didn’t do the first task, we have only done task 2 and 3. So for the task 2, it was divided
in two parts :CLEF 2023: : Conference and Labs of the Evaluation Forum, September 18-21, 2023, Thessaloniki, Greece$ quentindubreuil35@gmail.com (Q. Dubreuil)© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR

Workshop
Proceedings

http://ceur-ws.org
ISSN 1613-0073

CEUR Workshop Proceedings (CEUR-WS.org)

• Task 2.1 : Identification of the difficult terms• Task 2.2 : Explanation of the difficult terms
For the task 2.1, we used the pke[4, 5] package whereas for the task 2.2 we used the wikipediaapi package[6]. While for the task 3 we used the SimpleT5[7] model as well as the BLOOM[8]model.

2. Approach
For those tasks, we had 3 files :• A train file with the "source_snt" field• A qrels file with the "simplified_text" field• 3 test files of different size, small, medium and large. We only used the small and medium,not the large one for the sake of time, the large one has 150 000 translations to do, whereas
the small one has 2000 and the medium one has 4000, and the small is included in themedium and the medium in the large. Those 3 files have the "source_snt" field that need
to be translated.

2.1. Use of the pke package for the task 2.1
When we have used the pke package, we used those models :• FirstPhrases• TfIdf• YAKE [9]• TextRank [10]• SingleRank [11]• TopicRank [12]• PositionRank [13]First of all, we retrieve the data from the files and put it in a dataframe then we format it as
we need. We then initialize the model that we want to use to then apply it on every "source_snt".
Once we have 3 difficult words, we then determine its term difficulty rank within the sentence,as well as its complexity of the term for each of them.
Once we have all that, we format it with the required field for the task.

2.2. Use of the wikipedia-api package for the task 2.2
Initially, we import the Wikipedia package for the English language and proceed to retrieve the
Wikipedia page corresponding to the challenging terms obtained from task 2.1. Subsequently, we
utilize the nltk package, specifically the tokenization component, to extract the initial sentencefrom the retrieved pages. This initial sentence serves as our definition for the difficult term.
Then we format it with the required field for the task.

2.3. Use of SimpleT5 and BLOOM for the task 3
2.3.1. SimpleT5
The training part was divided into two parts, reading the data and formating it, and then the
training of the model.
We had to format the data to make two fields in a dataframe for our model, the "source_text"(train) and the "target_text" (test). The "source_text" was the "source_snt" field, and the "target_text" field was the "simplified_snt" field.
"source_text"
In the modern era of automation
and robotics, autonomous vehicles are currently the focus of academic and industrial research.

"target_text"
Current academic and industrial research is interested in autonomous vehicles.

Once we have our dataframe with the formated data, we can start the training of the SimpleT5
model, we first load the model, then we give this dataframe to our model. We have trained
our model for 12 epochs with a batch size of 8. Once the training is finished, we do the samething that we did before for the "source_text" and "target_text" but this time we only put the"source_snt" in the "source_text", and the model will infer the result in the "target_text". Thenwe get what was generated in the "target_text" field and we format it with the required fields
2.3.2. BLOOMFor the BLOOM model, we first try to use the inference api by sending request to the model on
huggingface, but this was really long, because we have to wait 1 hour after sometimes, so wetry to use it locally, but the model is really large and required a really powerful computer. So
we decided to use Petals[14, 15], a tool that help people to run large models collaboratively, andtherefore, BLOOM. Thanks to Petals we were able to use the BLOOM model without restrictions.
BLOOM is a few-shots model, therefore, we need to give him examples of what we need :• Summarize the source:
Source : In the modern era of automation and robotics, autonomous vehicles are currently
the focus of academic and industrial research.
Simplification : Current academic and industrial research is interested in autonomousvehicles.
Source : With the ever increasing number of unmanned aerial vehicles getting involved in
activities in the civilian and commercial domain, there is an increased need for autonomy
in these systems too.
Simplification : Drones are increasingly used in the civilian and commercial domain andneed to be autonomous.

Source : Due to guidelines set by the governments regarding the operation ceiling of civil
drones, road-tracking based navigation is garnering interest.
Simplification : Governments set guidelines on the operation ceiling of civil drones. So,
road-tracking based navigation is attracting interest.
Source :We then append the "source_snt" to this base, and we give the prompt to the BLOOM model,
we then get the simplification the model gives us. Once we have the simplification for eachsentence, we format it with the required fields.

3. Results
3.1. Task 2
3.1.1. Task 2.1
Every model of the pke package seem to be quite efficient, with the exception of the YAKE
model, which is a little below the others.
3.1.2. Task 2.2
The fact that we don’t have Wikipedia pages for all the difficult terms that we try to explain,makes this method not very appropriate. We have a BLEU score of 5.09, which is a really lowscore, and a ROUGE F1-score of 0.19, which is also really low.

3.2. Task 3
The results for the SimpleT5 model and the BLOOM model are quite similar, we have a FKGL
score of approximately 12, which does not answer the SimpleText task, the sentences are stillquite complex. For the SimpleT5 we have a SARI score of 30 and a BLEU score of 21, which is
not really convincing. And for the BLOOM model we have a SARI score of 37 and a BLEU scoreof 39 which is still not really convincing but a bit better than the SimpleT5 model.

4. Conclusion
Opting for the PKE Package was a prudent decision for indentifying difficult terms whereas
the Wikipedia approach is not something to consider in the future for explaining difficult
terms. While, for the task 3, both methods employed yielded satisfactory results, one that needs
training, and the other needs some examples, but the results were not really meaningful.

5. Acknowledgments
This project has received a government grant managed by the National Research Agency
under the program “Investissements d’avenir” integrated into France 2030, with the Reference
ANR-19-GURE-0001.

References[1] T. T. S. V. A. G. D. L. M. A. M. V. G. F. N. F. Avi Arampatzis, Evangelos Kanoulas (Ed.),
Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the
Fourteenth International Conference of the CLEF Association (CLEF 2023), 2023.
[2] L. Ermakova, E. SanJuan, J. Kamps, S. Huet, I. Ovchinnikova, D. Nurbakova, S. Araújo,
R. Hannachi, E. Mathurin, P. Bellot, Overview of the CLEF 2022 SimpleText Lab: Automatic
Simplification of Scientific Texts, in: A. Barrón-Cedeño, G. Da San Martino, M. Degli Esposti, F. Sebastiani, C. Macdonald, G. Pasi, A. Hanbury, M. Potthast, G. Faggioli, N. Ferro(Eds.), Experimental IR Meets Multilinguality, Multimodality, and Interaction, volume13390, Springer International Publishing, Cham, 2022, pp. 470–494. URL: https://link.
springer.com/10.1007/978-3-031-13643-6_28. doi:10.1007/978-3-031-13643-6_28,
series Title: Lecture Notes in Computer Science.
[3] L. Ermakova, E. SanJuan, S. Huet, O. Augereau, H. Azarbonyad, J. Kamps, CLEF 2023
SimpleText Track: What Happens if General Users Search Scientific Texts?, in: J. Kamps,
L. Goeuriot, F. Crestani, M. Maistro, H. Joho, B. Davis, C. Gurrin, U. Kruschwitz, A. Caputo(Eds.), Advances in Information Retrieval, volume 13982, Springer Nature Switzerland,Cham, 2023, pp. 536–545. URL: https://link.springer.com/10.1007/978-3-031-28241-6_62.
doi:10.1007/978-3-031-28241-6_62, series Title: Lecture Notes in Computer Science.
[4] F. Boudin, pke: an open source python-based keyphrase extraction toolkit, in: Proceedingsof COLING 2016, the 26th International Conference on Computational Linguistics: System
Demonstrations, The COLING 2016 Organizing Committee, Osaka, Japan, 2016, pp. 69–73.
URL: https://aclanthology.org/C16-2015.
[5] pke, 2016. URL: https://github.com/boudinfl/pke.
[6] wikipedia-api, 2017. URL: https://pypi.org/project/Wikipedia-API/.
[7] simpleT5, 2021. URL: https://github.com/Shivanandroy/simpleT5.
[8] B. Workshop, T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné,
A. S. Luccioni, F. Yvon, M. Gallé, J. Tow, A. M. Rush, S. Biderman, A. Webson, P. S.
Ammanamanchi, T. Wang, B. Sagot, N. Muennighoff, A. V. del Moral, O. Ruwase, R. Bawden,
S. Bekman, A. McMillan-Major, I. Beltagy, H. Nguyen, L. Saulnier, S. Tan, P. O. Suarez,
V. Sanh, H. Laurençon, Y. Jernite, J. Launay, M. Mitchell, C. Raffel, A. Gokaslan, A. Simhi,
A. Soroa, A. F. Aji, A. Alfassy, A. Rogers, A. K. Nitzav, C. Xu, C. Mou, C. Emezue, C. Klamm,
C. Leong, D. van Strien, D. I. Adelani, D. Radev, E. G. Ponferrada, E. Levkovizh, E. Kim, E. B.
Natan, F. De Toni, G. Dupont, G. Kruszewski, G. Pistilli, H. Elsahar, H. Benyamina, H. Tran,
I. Yu, I. Abdulmumin, I. Johnson, I. Gonzalez-Dios, J. de la Rosa, J. Chim, J. Dodge, J. Zhu,
J. Chang, J. Frohberg, J. Tobing, J. Bhattacharjee, K. Almubarak, K. Chen, K. Lo, L. Von Werra,

L. Weber, L. Phan, L. B. allal, L. Tanguy, M. Dey, M. R. Muñoz, M. Masoud, M. Grandury,
M. Šaško, M. Huang, M. Coavoux, M. Singh, M. T.-J. Jiang, M. C. Vu, M. A. Jauhar, M. Ghaleb,
N. Subramani, N. Kassner, N. Khamis, O. Nguyen, O. Espejel, O. de Gibert, P. Villegas,
P. Henderson, P. Colombo, P. Amuok, Q. Lhoest, R. Harliman, R. Bommasani, R. L. López,R. Ribeiro, S. Osei, S. Pyysalo, S. Nagel, S. Bose, S. H. Muhammad, S. Sharma, S. Longpre,
S. Nikpoor, S. Silberberg, S. Pai, S. Zink, T. T. Torrent, T. Schick, T. Thrush, V. Danchev,
V. Nikoulina, V. Laippala, V. Lepercq, V. Prabhu, Z. Alyafeai, Z. Talat, A. Raja, B. Heinzerling,
C. Si, D. E. Taşar, E. Salesky, S. J. Mielke, W. Y. Lee, A. Sharma, A. Santilli, A. Chaffin,
A. Stiegler, D. Datta, E. Szczechla, G. Chhablani, H. Wang, H. Pandey, H. Strobelt, J. A. Fries,
J. Rozen, L. Gao, L. Sutawika, M. S. Bari, M. S. Al-shaibani, M. Manica, N. Nayak, R. Teehan,
S. Albanie, S. Shen, S. Ben-David, S. H. Bach, T. Kim, T. Bers, T. Fevry, T. Neeraj, U. Thakker,
V. Raunak, X. Tang, Z.-X. Yong, Z. Sun, S. Brody, Y. Uri, H. Tojarieh, A. Roberts, H. W. Chung,J. Tae, J. Phang, O. Press, C. Li, D. Narayanan, H. Bourfoune, J. Casper, J. Rasley, M. Ryabinin,
M. Mishra, M. Zhang, M. Shoeybi, M. Peyrounette, N. Patry, N. Tazi, O. Sanseviero, P. vonPlaten, P. Cornette, P. F. Lavallée, R. Lacroix, S. Rajbhandari, S. Gandhi, S. Smith, S. Requena,
S. Patil, T. Dettmers, A. Baruwa, A. Singh, A. Cheveleva, A.-L. Ligozat, A. Subramonian,
A. Névéol, C. Lovering, D. Garrette, D. Tunuguntla, E. Reiter, E. Taktasheva, E. Voloshina,
E. Bogdanov, G. I. Winata, H. Schoelkopf, J.-C. Kalo, J. Novikova, J. Z. Forde, J. Clive, J. Kasai,
K. Kawamura, L. Hazan, M. Carpuat, M. Clinciu, N. Kim, N. Cheng, O. Serikov, O. Antverg,
O. van der Wal, R. Zhang, R. Zhang, S. Gehrmann, S. Mirkin, S. Pais, T. Shavrina, T. Scialom,
T. Yun, T. Limisiewicz, V. Rieser, V. Protasov, V. Mikhailov, Y. Pruksachatkun, Y. Belinkov,
Z. Bamberger, Z. Kasner, A. Rueda, A. Pestana, A. Feizpour, A. Khan, A. Faranak, A. Santos,
A. Hevia, A. Unldreaj, A. Aghagol, A. Abdollahi, A. Tammour, A. HajiHosseini, B. Behroozi,
B. Ajibade, B. Saxena, C. M. Ferrandis, D. Contractor, D. Lansky, D. David, D. Kiela,
D. A. Nguyen, E. Tan, E. Baylor, E. Ozoani, F. Mirza, F. Ononiwu, H. Rezanejad, H. Jones,
I. Bhattacharya, I. Solaiman, I. Sedenko, I. Nejadgholi, J. Passmore, J. Seltzer, J. B. Sanz,
L. Dutra, M. Samagaio, M. Elbadri, M. Mieskes, M. Gerchick, M. Akinlolu, M. McKenna,
M. Qiu, M. Ghauri, M. Burynok, N. Abrar, N. Rajani, N. Elkott, N. Fahmy, O. Samuel,R. An, R. Kromann, R. Hao, S. Alizadeh, S. Shubber, S. Wang, S. Roy, S. Viguier, T. Le,
T. Oyebade, T. Le, Y. Yang, Z. Nguyen, A. R. Kashyap, A. Palasciano, A. Callahan, A. Shukla,
A. Miranda-Escalada, A. Singh, B. Beilharz, B. Wang, C. Brito, C. Zhou, C. Jain, C. Xu,
C. Fourrier, D. L. Periñán, D. Molano, D. Yu, E. Manjavacas, F. Barth, F. Fuhrimann, G. Altay,
G. Bayrak, G. Burns, H. U. Vrabec, I. Bello, I. Dash, J. Kang, J. Giorgi, J. Golde, J. D. Posada,K. R. Sivaraman, L. Bulchandani, L. Liu, L. Shinzato, M. H. de Bykhovetz, M. Takeuchi,
M. Pàmies, M. A. Castillo, M. Nezhurina, M. Sänger, M. Samwald, M. Cullan, M. Weinberg,
M. De Wolf, M. Mihaljcic, M. Liu, M. Freidank, M. Kang, N. Seelam, N. Dahlberg, N. M.
Broad, N. Muellner, P. Fung, P. Haller, R. Chandrasekhar, R. Eisenberg, R. Martin, R. Canalli,
R. Su, R. Su, S. Cahyawijaya, S. Garda, S. S. Deshmukh, S. Mishra, S. Kiblawi, S. Ott, S. Sangaroonsiri, S. Kumar, S. Schweter, S. Bharati, T. Laud, T. Gigant, T. Kainuma, W. Kusa,
Y. Labrak, Y. S. Bajaj, Y. Venkatraman, Y. Xu, Y. Xu, Y. Xu, Z. Tan, Z. Xie, Z. Ye, M. Bras,
Y. Belkada, T. Wolf, BLOOM: A 176B-Parameter Open-Access Multilingual Language
Model, 2023. URL: http://arxiv.org/abs/2211.05100, arXiv:2211.05100 [cs].
[9] R. Campos, V. Mangaravite, A. Pasquali, A. Jorge, C. Nunes, A. Jatowt, YAKE! Keyword
extraction from single documents using multiple local features, Information Sciences

509 (2020) 257–289. URL: https://linkinghub.elsevier.com/retrieve/pii/S0020025519308588.
doi:10.1016/j.ins.2019.09.013.
[10] R. Mihalcea, P. Tarau, TextRank: Bringing Order into Text, in: Proceedings of the2004 Conference on Empirical Methods in Natural Language Processing, Association for
Computational Linguistics, Barcelona, Spain, 2004, pp. 404–411. URL: https://aclanthology.
org/W04-3252.
[11] X. Wan, J. Xiao, CollabRank: Towards a Collaborative Approach to Single-Document
Keyphrase Extraction, in: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Coling 2008 Organizing Committee, Manchester, UK,2008, pp. 969–976. URL: https://aclanthology.org/C08-1122.
[12] A. Bougouin, F. Boudin, B. Daille, TopicRank: Graph-Based Topic Ranking for Keyphrase
Extraction, in: Proceedings of the Sixth International Joint Conference on Natural Language
Processing, Asian Federation of Natural Language Processing, Nagoya, Japan, 2013, pp.
543–551. URL: https://aclanthology.org/I13-1062.
[13] C. Florescu, C. Caragea, PositionRank: An Unsupervised Approach to Keyphrase Extraction
from Scholarly Documents, in: Proceedings of the 55th Annual Meeting of the Association
for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 1105–1115. URL: https://aclanthology.org/P17-1102.
doi:10.18653/v1/P17-1102.
[14] petals-api, 2022. URL: https://github.com/bigscience-workshop/petals.
[15] A. Borzunov, D. Baranchuk, T. Dettmers, M. Ryabinin, Y. Belkada, A. Chumachenko,
P. Samygin, C. Raffel, Petals: Collaborative Inference and Fine-tuning of Large Models,
2023. URL: http://arxiv.org/abs/2209.01188, arXiv:2209.01188 [cs].

AppendicesA. Outputs example for Task 2.1

source_snt
In the modern era of automation and robotics, autonomous vehicles
are currently the focus of academic and industrial research.
In the modern era of automation and robotics, autonomous vehicles
are currently the focus of academic and industrial research.
In the modern era of automation and robotics, autonomous vehicles
are currently the focus of academic and industrial research.
With the ever increasing number of unmanned aerial vehicles getting
involved in activities in the civilian and commercial domain, there isan increased need for autonomy in these systems too.
With the ever increasing number of unmanned aerial vehicles getting
involved in activities in the civilian and commercial domain, there isan increased need for autonomy in these systems too.
Due to guidelines set by the governments regarding the operation
ceiling of civil drones, road-tracking based navigation is garneringinterest.
Due to guidelines set by the governments regarding the operation
ceiling of civil drones, road-tracking based navigation is garneringinterest.
In an attempt to achieve the above mentioned tasks, we propose an
imitation learning based, data-driven solution to UAV autonomy for
navigating through city streets by learning to fly by imitating an expert
pilot.
In an attempt to achieve the above mentioned tasks, we propose an
imitation learning based, data-driven solution to UAV autonomy for
navigating through city streets by learning to fly by imitating an expert
pilot.
Derived from the classic image classification algorithms, our classifier
has been constructed in the form of a fast 39-layered Inception model,that evaluates the presence of roads using the tomographic reconstructions of the input frames.
Derived from the classic image classification algorithms, our classifier
has been constructed in the form of a fast 39-layered Inception model,that evaluates the presence of roads using the tomographic reconstructions of the input frames.
Based on the Inception-v3 architecture, our system performs betterin terms of processing complexity and accuracy than many existingmodels for imitation learning.
Based on the Inception-v3 architecture, our system performs betterin terms of processing complexity and accuracy than many existingmodels for imitation learning.

termautonomous vehicles
modern era
industrial research
unmanned aerial vehicles

commercial domain

operation ceilinggovernments

data-driven solution

uav autonomy

classifier

form

terms

imitation learning

B. Outputs example for Task 2.2
source_snt
In the modern era of automation androbotics, autonomous vehicles are currently
the focus of academic and industrial research.
In the modern era of automation androbotics, autonomous vehicles are currently
the focus of academic and industrial research.
In the modern era of automation androbotics, autonomous vehicles are currently
the focus of academic and industrial research.
With the ever increasing number of unmanned aerial vehicles getting involved in
activities in the civilian and commercial domain, there is an increased need for autonomy in these systems too.
With the ever increasing number of unmanned aerial vehicles getting involved in
activities in the civilian and commercial domain, there is an increased need for autonomy in these systems too.
Recognizing Traffic Signs using intelligent
systems can drastically reduce the numberof accidents happening world-wide.

term

definition

modern era

The modern era is the period of human history that succeeds the Middle Ages (which
ended around 1500 AD) up to the present.

automation

Automation describes a wide range of technologies that reduce human intervention inprocesses, namely by predetermining decision criteria, subprocess relationships, andrelated actions, as well as embodying thosepredeterminations in machines.

robotics

Robotics is an interdisciplinary branch ofcomputer science and engineering.

number

A number is a mathematical object used tocount, measure, and label.

unmanned aerial
vehiclesAn unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without any human pilot, crew, or passengers on
board.

traffic signs

Recognizing Traffic Signs using intelligent
systems can drastically reduce the numberof accidents happening world-wide.

intelligent
tems

Recognizing Traffic Signs using intelligent
systems can drastically reduce the numberof accidents happening world-wide.

numbersys-

Traffic signs or road signs are signs erected at
the side of or above roads to give instructions
or provide information to road users.
Intelligent Systems Co., Ltd. is a Japanese
video game developer best known for developing games published by Nintendo with
the Fire Emblem, Paper Mario, WarioWare,and Wars video game series.
A number is a mathematical object used tocount, measure, and label.

C. Outputs example for Task 3
source_snt
In the modern era of automation and robotics, autonomous vehicles are currently the focus of academic and industrial research.
With the ever increasing number of unmanned aerial
vehicles getting involved in activities in the civilian
and commercial domain, there is an increased needfor autonomy in these systems too.
Due to guidelines set by the governments regarding
the operation ceiling of civil drones, road-tracking
based navigation is garnering interest.
In an attempt to achieve the above mentioned tasks,
we propose an imitation learning based, data-driven
solution to UAV autonomy for navigating throughcity streets by learning to fly by imitating an expert
pilot.
Derived from the classic image classification algorithms, our classifier has been constructed in the formof a fast 39-layered Inception model, that evaluates
the presence of roads using the tomographic reconstructions of the input frames.
Based on the Inception-v3 architecture, our system
performs better in terms of processing complexity
and accuracy than many existing models for imitation
learning.
The data used for training the system has been captured from the drone, by flying it in and around urban
and semi-urban streets, by experts having at least 6-8
years of flying experience.
Permissions were taken from required authorities
who made sure that minimal risk (to pedestrians) is
involved in the data collection process.
With the extensive amount of drone data that wecollected, we have been able to navigate successfully
through roads without crashing or overshooting, withan accuracy of 98.44The computational efficiency of
MAVNet enables the drone to fly at high speeds of
up to 6msec.
We present the same results in this research and compare them with other state-of-the-art methods ofvision and learning based navigation.

simplified_snt
In the modern era of automation and robotics, autonomous vehicles are the focus of research and development.
Drones are increasingly used in the civilian and commercial domain and need to be autonomous.
Governments set guidelines on the operation ceilingof civil drones. So, road-tracking based navigation is
attracting interest.
Researchers propose data-driven solutions allowing
drones to autonomously navigate city streets, learning to fly by imitating an expert pilot.

Our classifier uses a fast 39-layered model to evaluate
the presence of roads using the tomographic reconstructions of the input frames.

The Inception-v3 architecture has better accuracy
than many existing models of imitation learning.
The data used for training the system was captured
from the drone over urban streets, navigated by anexpert pilot.
Data collection requires special permission to ensure
pedestrian security.

MAVNet computational efficiency enables the drone
to fly up to 6m/sec.

The solution is compared with other recent methods.