ABSTRACT Illegal landfills are sites where garbage is dumped violating waste management laws. Aerial images enable the use of photo interpretation for territory scanning and landfill

detection but this practice is hindered by the manual nature of this task which also requires expert knowledge. Deep Learning methods can help capture the analysts’ expertise and build

automated landfill discovery tools. However, this goal requires public high-quality datasets for model training and testing. At present no such datasets exist and this gap penalizes the

research toward scalable and accurate landfill discovery methods. We present a dataset for landfill detection featuring airborne, WorldView-3, and GoogleEarth images annotated by

annotation tool. Measurement(s) Landfills detections in very high-resolution remote sensing imagery Technology Type(s) remote sensing imagery Sample Characteristic - Environment landfill

Sample Characteristic - Location Region of Lombardy SIMILAR CONTENT BEING VIEWED BY OTHERS MACHINE LEARNING-READY REMOTE SENSING DATA FOR MAYA ARCHAEOLOGY Article Open access 23 August 2023

MSLU-100K: A LARGE MULTI-SOURCE DATASET FOR LAND USE ANALYSIS IN MAJOR CHINESE CITIES Article Open access 07 May 2025 A 10 M RESOLUTION URBAN GREEN SPACE MAP FOR MAJOR LATIN AMERICAN CITIES

FROM SENTINEL-2 REMOTE SENSING IMAGES AND OPENSTREETMAP Article Open access 24 September 2022 BACKGROUND & SUMMARY Finding illegal landfills is a prominent objective of environmental

agencies but is a very complex and time-consuming task that requires sophisticated investigations. The increasing availability of aerial images has boosted the adoption of photo

interpretation for territory scanning and suspicious site identification1. However, large scale territory analysis is hindered by the manual nature of the photo interpretation task, which

must be conducted by experts. The advances in Computer Vision (CV) boosted by Deep Learning (DL) hold the promise to capture the expertise of professional photo interpreters and distill it

into computer-aided tools supporting territory monitoring at scale. The precondition for reaping the benefits of CV for landfill discovery is the availability of high-quality datasets for

training, validating, and testing predictive models. At present no such datasets exist for the landfill discovery task and this gap penalizes the research towards scalable and accurate

detection methods2,3,4,5,6. DL-based waste discovery in aerial images has been addressed by very few works7,8,9. These contributions illustrate the potential of DL for realizing image

interpretation pipelines at different scales and based on different CV tasks, such as scene classification and object detection. However, so far none of the related works has publicly

released the data set and the ground truth annotations employed for building the described architectures. The main reason for this restriction is the sensitivity of the data due to their

potential connection with investigation activities. General purpose datasets for aerial image/scene classification and object detection cannot help either. The most similar category found in

public datasets is that of _dump sites_ in the BigEarthNet repository10, which represents rather generic scenes and comprises only very small and coarse images. This limits the

replicability of research and ultimately the progress of the state of the art. The contribution of this work is the AerialWaste data set, a professionally curated dataset for landfills

discovery in aerial images, constructed based on the following criteria: * The images have different provenance and quality. * Images are associated with annotations that specify the

presence of a waste dump as a whole and the visibility of specific types of garbage. * The annotations are curated by professional photo interpreters specialized in the use of aerial images

for landfill detection. * The provided annotations can be used as ground truth (GT) labels for multiple CV tasks: binary and multi-class image classification and weakly supervised

localization. * The dataset adheres to the standard MS COCO format11. AerialWaste contains 10,434 images generated from tiles of three different sources: AGEA Ortophotos (≈20 cm GSD),

WorldView-3 (≈30 cm GSD) and GoogleEarth (≈50 cm GSD). Of these images 3,478 are positive examples representing locations considered suspicious and 6,956 are negative examples. Most positive

instances are characterized by metadata about the evidence, severity, and area type of the site. A subset of 715 samples is annotated with the class of the waste objects visible in the

image chosen among 22 different categories. 169 images are provided with segmentation masks (from which bounding boxes are automatically extracted) that surround the objects of 9 garbage

classes. The data set and its documentation can be accessed from the website www.aerialwaste.org. METHODS AerialWaste is the result of a collaboration with the professional photo

interpreters and territory monitoring experts of ARPA Lombardia, the environment monitoring agency of Region Lombardy (Italy). Figure 1 illustrates the creation and technical validation

process of the AerialWaste dataset. The dataset creation phase exploited the experts’ knowledge in order to produce a collection of images divided into positive and negative samples and

enriched with meta-data and segmentation masks. The professional photo interpreters scanned a geographic region and built a database of relevant locations where waste dumps were visible.

From such a database the actual image collection was created. Samples corresponding to the selected locations were extracted from the three above-mentioned aerial image sources and tagged as

positive. The data set was completed with negative samples extracted from the same sources at randomly chosen locations. The positive sites were annotated with meta-data defined by the

experts, including the type of waste visible, its storage mode, the type of the area around the site, the degree of evidence, and the severity of the illicit. The technical validation phase

was conducted by using the AerialWaste images and annotations to train, validate and test a state-of-the-art binary scene classification model for detecting the presence of waste. The model

trained on images from all the three heterogeneous sources achieves 87.9% average precision on the test set. The model trained only on high-quality orthophotos attains 94.5% average

precision on the test set. These results show the effectiveness of the AerialWaste data set for training waste discovery models. DATA SET CREATION AerialWaste copes with the visual

heterogeneity of the scenes in which waste dumps occur in aerial images and with the diverse nature of the objects that compose a waste deposit. When observed from above waste dumps appear

as complex arrangements of objects of different shapes, sizes, and orientations: a typical case occurs when a shed or a dismissed industrial plant is filled with waste which can be seen

spilling over the construction boundaries. The area may also contain other clues, such as sparse debris, pallets, or containers. Further signs can be trucks, the isolation of the place,

secondary access roads, and stressed vegetation12. Typical materials found in dumping sites include organic waste, plastics, glass, metal, paper, wood, textiles, tires, bulky waste,

electronics, and asbestos panels13. Figure 2 presents some examples of how waste dumps appear in aerial images. THE LOCATION DATABASE The creation of the dataset starts with the definition

of a database of locations identified as containing waste dumps by four territory monitoring professionals of the Environmental Protection Agency of Region of Lombardy (ARPA). The database

comprises 10,434 locations belonging to 487 different municipalities of the Lombardy region in Italy. Of these 33% are sites reported by the analysts as positive, i.e., hosting some form of

illegal waste disposal. The remaining 67% are randomly sampled in the territory of the same municipalities. To account for the class imbalance, the negative locations are twice as many as

the positive ones. The negative instances are obtained by randomly picking two locations at a distance _d_ from each positive instance (with 1 km ≤ _d_ ≤ 5 km). The lower threshold of 1 km

is selected to avoid including a positive site in a negative image, whereas the upper threshold of 5 km is selected heuristically so that the negative sample is likely to remain in an area

with the same land use as the positive one. All data points are characterized by the geographic coordinates (latitude and longitude). Most sites have additional metadata obtained from the

ARPA database: the evidence (how many indicators of non-compliance with waste disposal regulations are detected by the analyst) and severity (how critical the non-compliance is perceived by

the analyst) expressed in the 0 (low) to 3 (high) range, the type of visible objects (e.g., tires, scrap), the storage mode (e.g., delimited heaps, containers) and the category of the area

(e.g., production plant, agricultural area). The location database is used to generate the actual image dataset and is not disclosed for confidentiality reasons. IMAGES GENERATION For each

location in the database one square image is created centered at that location and covering an area of 210 × 210 meters. The images are drawn from tiles of three provenance sources: * AGEA:

orthophotos generated by an airborne campaign executed by the Italian Agriculture Development Agency AGEA. This source contains the images produced by the acquisition campaign launched in

2018 in the Lombardy Region (Italy). An RGB aerophotogrammetry survey was conducted at a spatial resolution of ≈20 cm GSD. The size of the resulting images is ≈1000 × 1000 pixels. *

WorldView-3 (WV3): high-resolution pan-sharpened RGB images acquired by a commercial satellite sensor (no pan-sharpened near infrared images were used). The collection corresponds to the

campaign conducted in 2021 at a spatial resolution of ≈30 cm GSD. The size of the resulting images is ≈700 × 700 pixels. * Google Earth (GE): images downloaded using the Google API. Spatial

resolution is ≈50 cm GSD. The size of the images is ≈1000 × 1000 pixels as a result of the up-sampling by a scale of 2 performed by the API. Google images are free to the public and have

been used in different remote sensing studies14,15,16,17,18. Their use must respect the Google Earth terms and conditions19. The API provides only the most recent imagery and thus small

changes can be observed when an image is updated after the photo interpreter has annotated it. Figure 3 shows some examples of positive locations from different sources. The images present a

high intra-class diversity: (1) the type of area includes isolated sites, urban locations and industrial areas; (2) the visible waste objects are extremely heterogeneous; (3) the spatial

arrangement and the scale of the waste dumps in the scenes are diverse. Figure 4 shows the distribution of positive and negative images over the provenance sources. Most examples are drawn

from Google Earth, followed by AGEA orthophotos. Fewer samples are drawn from the WorldView-3 collection because these were acquired only recently (in 2021). Figure 5 shows the distribution

of the evidence, severity, and area type properties across the positive samples. These characteristics are not used for training the CV component but are mentioned to show the heterogeneity

of the dataset, which contributes to the complexity of the waste detection task. The evidence attribute is present in most samples, and the severity and area type annotations are specified

for ≈72% of the samples. META-DATA DEFINITION AND DATA SET ANNOTATION The type of waste and its visual appearance are expressed by means of two annotations: the type of visible objects (TO)

and the storage mode (SM). Table 1 lists the different TO and SM labels used by the experts and reports the number of samples annotated with them. Some of the categories used by the experts

in their current interpretation activity are equivalent to the standard codes proposed by the European Union (e.g., “07.5-Wood wastes” or “07.4-Plastic wastes”)20. The current categories

have been chosen by the photo interpreters as those most clearly distinguishable in the aerial images. The expansion of the data set annotations to comply with the standard EU codes is

planned as a future activity. Figure 6 presents examples of images annotated with different TO and SM labels. While some objects such as bulky items, excavated earth, tires, and vehicles are

easy to recognize, some others such as scrap or plastics require a more trained eye. The storage modes are more distinguishable. Figure 7 shows the distribution of the images across the TO

and SM classes and with respect to the provenance sources. Most annotations are associated with images drawn from the Google Earth source. Figure 8 shows the co-occurrence of the most common

TO and SM labels and of the area types. For example, the rubble/excavated earth and rocks class is mostly present in degraded areas. For a subset of 169 images belonging to the test set

only, the segmentation mask were generated with the ODIN annotation tool21 by drawing polygons around the relevant image region. Table 2 lists the 9 categories associated with the

segmentation masks and the number of masks per category. Figure 9 shows some examples of the available annotated segmentation masks. DATA RECORDS The AerialWaste dataset is published in the

Zenodo repository22 and comprises the following artifacts, which constitute the public part of the dataset: * Images folder: it contains the images corresponding to each location in the

database. The images are not geo-referenced because the coordinates are considered sensitive information. * Dataset description file: it describes the images in the data set using the JSON

format and following the MS COCO guidelines. The description specifies the provenance and metadata of each image. TECHNICAL VALIDATION The technical validation of AerialWaste has focused on

the adequacy of the dataset for building DL predictive models able to support analysts in the discovery of waste dump sites. TRAINING AND TESTING OF A WASTE BINARY CLASSIFIER To cope with

the complexity of landfill imagery, in which the recognition of the relevant scenes might need a varying degree of context (e.g., garbage stored in dumpsters vs. scattered in a large area),

we trained a multi-scale CNN architecture normally employed in complex scene detection tasks and tested it on a large-scale territory, reporting both qualitative and quantitative evaluation

results. The validation procedure can be summarized as follows: * The dataset is divided in two splits: 75% of the images for training and 25% for testing. * The train set of the AerialWaste

data set was used to train a binary CNN classifier featuring a ResNet50 backbone augmented with Feature Pyramid Network (FPN) links23, a technique that improves the detection of items at

different scales. * We evaluated the performance of the architecture on the AerialWaste test set. The binary classifier achieved 87.99% average precision and 80.70% F1 score, with 81.89%

precision at 79.54% recall. Figure 10 shows the variation of the classification performances across the different data sources. * We also assessed the impact of image quality by training and

testing the same architecture only on the AerialWaste high quality AGEA ortophotos. In this case, the binary classifier achieves 94.5% average precision and 88.2% F1 score. The details of

such experiment are reported in9. * The output of the classifier was further validated by experts with the help of Class Activation Maps (CAMs)24 to highlight the image regions where the

classifier focused its attention. CLASSIFICATION ARCHITECTURE The binary classifier exploits ResNet5025 as the backbone and augments it with a Feature Pyramid Network (FPN) architecture26.

The FPN improves object detection performances when different scales must be considered27,28 and can benefit also classification tasks in which objects of the same class appear with variable

sizes. The multi-scale feature pyramid is obtained by combining low resolution semantically strong features with high resolution semantically weaker ones. This is realized by complementing

the bottom up feature extraction path typical of CNNs with a top down path that builds the feature pyramid by extracting, adapting, and merging features at multiple levels. Figure 11 shows

the structure of the FPN architecture added to the ResNet50 backbone. The initialization of ResNet50 was performed with transfer learning from ImageNet29,30. The best results were obtained

by freezing the first two layers during the fine-tuning. In the training phase, data augmentations (flip, rotation, and crop) were applied and the input images were resized to a fixed

dimension to cope with different sizes in the same batch and to optimize GPU memory usage. The pixel values were also normalized based on the mean and standard deviation of the data set.

After the last FC classification layer, a Sigmoid function is used to obtain a value between 0 and 1, which denotes how confident the model is that the image belongs to the positive

class30,31. Additional details on how the training was performed are available in the Git repository of the model. A threshold of 0.5 over such a value was used to classify each image.

QUALITATIVE ASSESSMENT AND EMPIRICAL VALIDATION WITH PROFESSIONAL PHOTO INTERPRETER A visual inspection of the results helps understand the quality of the predictions. To understand which

objects are responsible for classifying a site as a waste dump, Class Activation Maps (CAMs)24 were computed. CAMs are matrices in which each cell contains a value denoting the relevance of

the pixel with respect to the class of interest. Figure 12 shows some examples of correctly predicted images overlaid with the heat maps derived from the respective CAMs. The first example

illustrates the AGEA image of a residential area in which a backyard contains a small dump of sparse items. The model focuses on the relevant spot, as shown by the CAM heat map. The second

example is a WV3 image of a production site with bulky items, well delineated by the CAM heat map. The third example is a GE image of a site disseminated with scrap, plastic, and other

unidentified objects, which are correctly highlighted by the heat map peak regions. The binary classifier trained with the AerialWaste data set was used by ARPA Lombardy analysts to assist

in the scanning of the AGEA imagery of a novel territory. A dedicated Inspection Tool was implemented to ease the visualization of the aerial photos, of the model predictions, and of the

CAMs. A total of 69 sites were identified as containing waste dumps, of which 65 corresponded to locations predicted by the CNN model as relevant. The model trained only on AGEA orthophotos

classified all the 69 locations as relevant. Approximately 50% of the identified sites were located in scenes with a confidence score of the positive class higher than 0.75. The global

average confidence score of the sites reported by the analysts is 0.66. USAGE NOTES DATASET USAGE The dataset can be used to train models for the binary and multi-label classification tasks

and for the weakly supervised localization task. The AerialWaste website provides the link to the public repository to download the dataset. Utilities to plot the dataset statistics (e.g.,

the distribution diagrams presented in this paper) and to visualize it are also provided. A utility class extending the

https://pytorch.org/docs/stable/data.html#torch.utils.data.DatasetPyTorch DataSet class is also provided to load the dataset with the PyTorch data processing library. USAGE WITH THE ODIN

DIAGNOSTIC TOOL ODIN21,32 is an open source diagnosis framework for generic ML classification tasks and for CV object detection and instance segmentation tasks. ODIN lets developers add

custom annotations to their data sets, compute performance metrics split by annotation values, and visualize diagnosis reports. Its annotation functionality was used to draw the segmentation

masks illustrated in Fig. 9. The AerialWaste Git repository contains scripts that exploit the ODIN tool for: * Importing the dataset and obtaining statistics (e.g. samples per class,

distribution of annotation values). * Visualizing the images, with a function to browse the samples and a form-based interface to select them based on a user-defined criterion. * Visualizing

the segmentation masks, with a function to browse the samples annotated with a mask and a form-based interface to select those that meet a user-defined criterion. Furthermore, when

predictions are available, ODIN can be exploited to compute reports of the model performances and to visualize the CAMs and the performance metrics. USAGE WITH THE INSPECTION TOOL The

AerialWaste dataset is equipped with an Inspection Tool that supports the visualization of predictions of waste detectors. Its main functions are: * Map exploration. The user can move around

a map of the region of interest by dragging the mouse to different positions or by using an iteration function. The iteration mode superimposes a grid over the area under analysis and lets

the user move by changing the grid position with the previous and next commands. The GUI also offers the usual zoom controls. * Predictions visualization. The user can display the previously

computed model output on the map. Predictions are shown by means of color-coded rectangles, where the color indicates the confidence of the model. Very low confidence predictions (below

0.20) are filtered out. * CAMs Visualization. The user can visualize the CAMs associated with the predictions. This function helps identify the most relevant spots and prevents the oversight

of suspicious objects. * Source selection. The user can choose the map layer among the configured sources. Each source may provide different information. For example, the Google Maps layer

provides street names and references to different points of interest, which can be useful to better understand the context. * Mask definition. The user can mark the presence of waste objects

by drawing a polygon around the relevant areas. * Annotation creation. The user can add or modify metadata about a location. The descriptive fields by default include severity, evidence,

environmental risk, waste type, and waste storage mode, plus a free text input for additional information. * Data export. The user can export the session data in different formats: as a

Comma Separated Values (CSV) file to visualize in a spreadsheet or as a Keyhole Markup Language (KML) file to display in a GIS (e.g., QGIS). Figure 13 shows the map interface of the

Inspection Tool and Fig. 14 illustrates the interface for creating and annotating a site. SUPPORTED USE CASES AND EXTENSION The AerialWaste dataset is offered to the scientific community for

promoting the progress of computer-aided waste discovery in aerial images. It is the first public collection of aerial images portraying landfills and other types of waste dumps curated by

professional photo interpreters and made available publicly. AerialWaste can be used “as is” to train a CV model for the binary (waste/no waste) classification of aerial images. From an

application perspective, we found that this level of intelligence is sufficient for environmental agencies seeking to accelerate their territory monitoring processes. When a location is

detected as suspicious, the next step is an on-site recognition to ascertain whether the detection is an actual true positive. However, when the area to monitor is large and densely

populated the number of positive identifications quickly grows and makes inspecting all the potentially relevant sites impossible. In this case, a multi-class object-based classification is

a better aid, because the type of detected waste objects may support the assessment of risk and the prioritization of interventions. Accurate object localization is the next usage level. In

this case, the position and the number of relevant objects can be estimated, which further contributes to proper characterization of the site from remote. The present version of AerialWaste

can support the training and testing of weakly supervised localization (WSL) architectures33,34 by providing segmentation masks for the images in the test set. Given the high cost of

creating object-level masks34 we have added to the dataset a number of segmentation masks sufficient to evaluate a WSL model trained with the provided whole-image labels of the object types.

We expect that this feature will promote the development of more discriminative predictors advancing the state of the art in computer-aided waste discovery and environmental risk

assessment. The planned extensions of AerialWaste pursue different directions: * Geographical expansion. The present locations belong to the same Italian region (Lombardy), which albeit very

diversified in terms of territory configuration and land use may induce some selection bias. We plan to extend the collaboration to environment agencies in other territories so as to

improve the dataset diversity. * Hard negative sampling. Besides intra-class diversity, aerial images of landfills and of waste disposal sites have also high inter-class similarities. An

operational production plant sometimes can be confounded with an abandoned one used for illegal waste storage. We plan to search hard negative examples and add them to the data set. *

Multi-modal imagery. AerialWaste contains only RGB images. The addition of samples acquired with different remote sensing products beyond the visible band could support the design of

multi-modal detectors, e.g., exploiting the NIR band to identify the presence of stressed vegetation as a clue for buried waste. * Multi-temporal imagery. Some sources archive aerial images

acquired over time. Analyzing images taken at different dates could provide information on the site activity, e.g., growing or shrinking. Adding time series of images of the same location to

AerialWaste may help environmental agencies to plan interventions better, e.g., by prioritizing missions based on the growth rate of the dumping sites. * Additional metadata. The metadata

currently present in AerialWaste are the result of the annotations that experts routinely create in order to characterize the site and are not conceived with the specific purpose of

supporting machine learning tasks. We plan to improve the metadata by adhering to the EU waste codes20 so as to foster interoperability with future waste discovery datasets and by adding

technical annotations supporting the diagnosis of machine learning models. * Object localization. We are training a WSL component for detecting waste objects with the whole image labels of

the data set. We plan to use the segmentation masks output by such a detector as a starting point for the next round of expert annotations. We think that the availability of object mask

proposals, albeit not fully accurate, can accelerate the annotation task substantially. CODE AVAILABILITY The data presented in this study, as well as the list of tools employed, are

publicly released: • AerialWaste toolkit: Instructions and scripts to visualize it and draw basic statistics are published in the following repository

https://github.com/nahitorres/aerialwaste. • AerialWaste model: the models and the code to execute them are released on https://github.com/nahitorres/aerialwaste-model. • Inspection Tool:

the Jupyter notebook-based tool is also publicly released on https://github.com/nahitorres/demo-inspection-tool. After the execution of the models in new areas, the predictions can be

visualized with the inspection tool. • ODIN: the tool used for the dataset annotation process and for the model evaluation phase is a previous work, available at

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(2021). Download references ACKNOWLEDGEMENTS We wish to thank the Environmental Protection Agency of the Region of Lombardy (ARPA Lombardia) for the collaboration in the acquisition of the

imagery and for providing the private location database. This work has been partially supported by the European Union’s Horizon 2020 project PERIVALLON, under grant agreement No. 101073952.

AUTHOR INFORMATION Author notes * These authors contributed equally: Rocio Nahime Torres, Piero Fraternali. AUTHORS AND AFFILIATIONS * Politecnico di Milano, Department of Electronics

Information and Bioengineering, Milan, 20133, Italy Rocio Nahime Torres & Piero Fraternali Authors * Rocio Nahime Torres View author publications You can also search for this author

inPubMed Google Scholar * Piero Fraternali View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS All authors contributed to the study conception

and design, as well as the writing of the manuscript. Data preparation and visualization and technical validation were carried out R.N.Torres and supervised by P. Fraternali. Software was

procured by R.N.Torres. CORRESPONDING AUTHORS Correspondence to Rocio Nahime Torres or Piero Fraternali. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests.

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holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Torres, R.N., Fraternali, P.

AerialWaste dataset for landfill discovery in aerial and satellite images. _Sci Data_ 10, 63 (2023). https://doi.org/10.1038/s41597-023-01976-9 Download citation * Received: 06 September

2022 * Accepted: 18 January 2023 * Published: 31 January 2023 * DOI: https://doi.org/10.1038/s41597-023-01976-9 SHARE THIS ARTICLE Anyone you share the following link with will be able to

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